diff --git a/book/_config.yml b/book/_config.yml
index 136c1d871fa7865b62d7e01b32c514e372a3ccca..eeb4cb9d7e893bd4e7b05da335e258fe7d415014 100644
--- a/book/_config.yml
+++ b/book/_config.yml
@@ -20,8 +20,8 @@ sphinx:
     - https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js
     thebe_config:
       use_thebe_lite: true
-      exclude_patterns: ["**/*"]
-      # exclude_patterns: ["**/_*.yml", "**/*.md", "**/*.ipynb", "**/*.zip", "**.gif", "**/*.jpg", "**/*.tif", "**/*.shp", "**/*.pickle", "**/*.geojson"]
+      # exclude_patterns: ["**/*"]
+      exclude_patterns: ["**/_*.yml", "**/*.md", "**/*.ipynb", "**/*.zip", "**.gif", "**/*.jpg", "**/*.tif", "**/*.shp", "**/*.pickle", "**/*.geojson"]
     html_theme_options:
       repository_url: "https://gitlab.tudelft.nl/mude/book"
       use_issues_button : true
diff --git a/book/_toc.yml b/book/_toc.yml
index 618f202e063b1c14ba070c83912515dce78fe956..e840e3e16fdcf86408268baf3178bfe10c4d0e1e 100644
--- a/book/_toc.yml
+++ b/book/_toc.yml
@@ -163,35 +163,27 @@ parts:
     - file: time_series/intro.md
       title: Time Series Analysis
       sections:
-      - file: time_series/components
+      - file: time_series/components.md
         title: "Time Series components"
         sections:
         - file: time_series/exercise1.ipynb
-      - file: time_series/types
-        title: "Types of Time Series"
+      - file: time_series/noise.ipynb
+        title: "Noise and stochastic model"
       - file: time_series/modelling
         title: "Time Series modelling and estimation"
         sections:
-        - file: time_series/exercise3.ipynb
+        - file: time_series/fit_BLUE.ipynb
       - file: time_series/stationarity
         title: "Stationarity"
-        sections:
-        - file: time_series/exercise2.ipynb
       - file: time_series/acf
         title: "Autocovariance function (ACF)"
+      - file: time_series/ar.ipynb
+        title: "AutoRegressive Moving Average (AR) process"
         sections:
-        - file: time_series/exercise4.ipynb
-      - file: time_series/arma
-        title: "AutoRegressive Moving Average (ARMA) process"
-        sections:
-        - file: time_series/exercise5.ipynb
+        - file: time_series/ar_exercise.ipynb
       - file: time_series/forecasting
         title: "Time Series forecasting"
-      - file: time_series/optional
-        title: "Supplementary material"
       # START REMOVE-FROM-PUBLISH
-      - file: time_series/notebook
-        title: Notebook
       # END REMOVE-FROM-PUBLISH
     # END REMOVE-FROM-PUBLISH
 
diff --git a/book/time_series/acf.md b/book/time_series/acf.md
index 9e40a42fd0472cf3d8f4c5fba183fbee41d00b9d..ab6659d18c8d0b23f32d51543d30abda7ee3f25b 100644
--- a/book/time_series/acf.md
+++ b/book/time_series/acf.md
@@ -1,36 +1,82 @@
 (ACF)=
 # Autocovariance function (ACF)
 
-Before we can look into the modelling of a stochastic process using an Autoregressive Moving Average (ARMA) model, we first need to introduce the autocovariance function (ACF) for a stationary time series, and describe the relationship between ACF and a power spectral density (PSD).
+Before we can look into the modelling of a stochastic process using an Autoregressive (AR) model, we first need to introduce the autocovariance function (ACF) for a stationary time series, and describe the relationship between ACF and a power spectral density (PSD).
 
-As in the Chapter on {ref}`OT`, the variance component is often determined based on the precision of an observation (at a given epoch), and the covarience components quantitatively indicate the statistical dependence (or independence) between observations. In this case, dependence is inherently introduced by the phyiscal processes that produce the signal (of which our time series is a sample), and in fact our time series methods seek to (mathematically) account for this.
-
-A preliminary motivation and explanation for why autocovariance is needed is provided nicely in this lecture, beginning at time 1:22:32 (and ending around 1:34:00).
-
-<p><iframe width="680" height="480" marginwidth="0" marginheight="0" src="https://collegerama.tudelft.nl/Mediasite/Play/1d74f018b9b54e918179570c75f6cd0c1d?playFrom=4952000&autostart=False"></iframe></p>
+As in [Observation theory](../observation_theory/01_Introduction.md), the variance component is often determined based on the precision of an observation (at a given epoch), and the covariance components quantitatively indicate the statistical dependence (or independence) between observations. In this case, dependence is inherently introduced by the physical processes that produce the signal (of which our time series is a sample), and in fact our time series methods seek to (mathematically) account for this.
 
 ## Autocovariance and autocorrelation
 
-Let us assume an arbitrary (discrete) stationary time series, $S=[S_1,S_2,...,S_m]^T$, with mean $\mathbb{E}(S)=\mu$ and variance $Var(S_{i})=\sigma^2$.
+Let us assume an arbitrary (discrete) stationary time series, $S=[S_1,S_2,...,S_m]^T$, with mean $\mathbb{E}(S)=\mu$ and variance $Var(S_{i})=\sigma^2$. Remember that stationarity implies that the statistical properties of the time series do not depend on the time at which it is observed, i.e. expectation and variance are constant over time.
 
 The *formal* (or: theoretical) autocovariance is defined as
 
 $$
-Cov(S_t, S_{t-\tau}) = \mathbb{E}((S_t-\mu)(S_{t-\tau}-\mu))=\mathbb{E}(S_tS_{t-\tau})-\mu^2
+Cov(S_t, S_{t+\tau}) =\mathbb{E}(S_tS_{t+\tau})-\mu^2
 =c_{\tau}
 $$
 
 We have that $Cov(S_t, S_{t-\tau}) =Cov(S_t, S_{t+\tau})$.
 
 
-```{note}
-The reason to refer to *auto*covariance is that we are considering the covariance of $S$ with itself (with a certain time lag $\tau$). If the covariance of $S$ with the time series of another variable, $X$, would be considered, this is referred to as the *cross*-covariance. 
-```
+:::{card} Exercise covariance
+
+Show that the covariance can be written as: 
+
+$$Cov(S_t, S_{t+\tau}) = \mathbb{E}(S_tS_{t+\tau})-\mu^2
+=c_{\tau}$$ 
+
+
+````{admonition} Solution
+:class: tip, dropdown
+
+$$
+ Cov(S_t, S_{t+\tau})= \mathbb{E}[(S_t - \mathbb{E}(S_t))(S_{t+\tau} - \mathbb{E}(S_{t+\tau}))]\\
+ = \mathbb{E}((S_t-\mu)(S_{t+\tau}-\mu))\\
+ = \mathbb{E}(S_tS_{t+\tau} - \mu S_{t+\tau} - \mu S_t + \mu^2)\\
+ = \mathbb{E}(S_tS_{t+\tau}) - \mu \mathbb{E}(S_{t+\tau}) - \mu \mathbb{E}(S_t) + \mu^2\\
+= \mathbb{E}(S_tS_{t+\tau}) - 2\mu^2 + \mu^2\\
+= \mathbb{E}(S_tS_{t+\tau}) - \mu^2\\
+$$
+````
+:::
+
+:::{card} Exercise covariance
+
+Prove that $Cov(S_t, S_{t-\tau}) =Cov(S_t, S_{t+\tau})$: 
+
+
+````{admonition} Solution
+:class: tip, dropdown
+
+From the definition of covariance, we know that
+
+$$
+Cov(a,b) = Cov(b,a)
+$$
+
+Hence, we have that
+
+$$ Cov(S_t, S_{t-\tau}) = Cov(S_{t-\tau}, S_t)$$
+
+Due to the stationarity of the time series, we have that
+
+$$ Cov(S_{t-\tau}, S_t) = Cov(S_t, S_{t+\tau})$$
+
+Therefore, we have that
+
+$$ Cov(S_t, S_{t-\tau}) = Cov(S_t, S_{t+\tau})$$
+
+
+````
+:::
+
+
 
 The *formal* autocorrelation is defined as
 
 $$
-r_{\tau} = \mathbb{E}(S_tS_{t-\tau})
+r_{\tau} = \mathbb{E}(S_tS_{t+\tau})
 $$
 
 ```{note}
@@ -46,12 +92,12 @@ The autocovariance function of a time series is not known beforehand, and hence
 For a given stationary time series $S = [S_1,S_2,...,S_m]^T$, the least-squares estimator of the **autocovariance function** is given by
 
 $$\
-\hat{C}_{\tau} = \frac{1}{m-\tau}\sum_{i=1}^{m-\tau}(S_i-\mu)(S_{i+\tau}-\mu), \hspace{25px} \tau=0,1,...,m-1$$
+\hat{C}_{\tau} = \frac{1}{m-\tau}\sum_{i=1}^{m-\tau}(S_{i+\tau}-\mu)(S_{i}-\mu), \hspace{25px} \tau=0,1,...,m-1$$
 
 The least-squares estimator of **autocorrelation** (also called empirical autocorrelation function) is then
 
 $$
-\hat{R}_{\tau}=\frac{1}{m-\tau}\sum_{i=1}^{m-\tau}S_i S_{i+\tau}, \hspace{25px} \tau=0,1,...,m-1
+\hat{R}_{\tau}=\frac{1}{m-\tau}\sum_{i=1}^{m-\tau}S_{i+\tau} S_{i}, \hspace{25px} \tau=0,1,...,m-1
 $$
 
 **Maximum likelihood estimations**
@@ -59,7 +105,7 @@ $$
 The maximum likelihood estimator of **autocovariance** is given by
 
 $$
-\hat{C}_{\tau} = \frac{1}{m}\sum_{i=1}^{m-\tau}(S_i-\mu)(S_{i+\tau}-\mu), \hspace{25px} \tau=0,1,...,m-1
+\hat{C}_{\tau} = \frac{1}{m}\sum_{i=1}^{m-\tau}(S_{i+\tau}-\mu)(S_i-\mu), \hspace{25px} \tau=0,1,...,m-1
 $$
 
 Note that this is a biased estimator, $\mathbb{E}(\hat{C}_{\tau})\neq c_{\tau}$.
@@ -67,17 +113,13 @@ Note that this is a biased estimator, $\mathbb{E}(\hat{C}_{\tau})\neq c_{\tau}$.
 Similarly, the maximum likelihood estimate of the autocorrelation follows as:
 
 $$
-\hat{R}_{\tau}=\frac{1}{m}\sum_{i=1}^{m-\tau}S_i S_{i+\tau}, \hspace{25px} \tau=0,1,...,m-1
+\hat{R}_{\tau}=\frac{1}{m}\sum_{i=1}^{m-\tau}S_{i+\tau} S_{i}, \hspace{25px} \tau=0,1,...,m-1
 $$
 
 ```{note}
 Here we use capitals for $\hat{C}_{\tau}$ and $\hat{R}_{\tau}$ since **estimators** are always a function of the random observables $S_t$.
 ```
 
-```{note}
-Software tools may have implemented one or both methods to choose from, so if possible good to check!
-```
-
 ### Covariance matrix based on autocovariance
 
 The structure of a covariance matrix for a stationary time series is purely symmetric and it looks like
@@ -91,9 +133,6 @@ c_2 & c_1 & \sigma^2 &  \ddots & c_2  \\
 
 There are $m$ (co)variance components - **one** variance component, $\sigma^2 = c_0$, and $m-1$ covariance components, $c_i$.
 
-```{note}
-The covariance matrix $\Sigma_{S}$ has constant values along the top-left to bottom-right diagonal and is called a _Toeplitz matrix._
-```
 
 (NACF)=
 ## Normalized ACF
@@ -225,40 +264,3 @@ $$
 
 ```
 :::
-
-## Power spectral density
-
-The power spectral density (PSD) explains how the power (variance) of the signal is distributed over different frequencies. For instance, the PSD of a pure sine wave is flat *except* at its constituent frequency, where it will show a peak. Purely random noise has a flat power spectrum, indicating that all frequencies have an identical contribution to the variance of the signal!
-
-### PSD vs ACF
-
-Knowledge on ACF, in time domain, is mathematically equivalent to knowledge on PSD, in the frequency domain, and vice-versa. And, from here, you might have a clue of where this is taking us... The PSD is the **discrete Fourier transform (DFT)** of the ACF.
-
-$$
-\begin{align*}
-\text{DFT}(\hat{c}_{\tau})&=\text{DFT}\left(\frac{1}{m}\sum_{i=1}^m s_i s_{i+\tau}\right)\\&=\frac{1}{m\Delta t}F_S(k) F_S(k)^*\\&=\frac{1}{m\Delta t}|F_S(k)|^2
-\end{align*}$$
-
-where the Fourier coefficients (see [DFT section](FFT)) are:
-
-$$F_S(k)  = \Delta t\sum_{i=1}^my_ie^{-j\frac{2\pi}{m}(k-1)(i-1)}$$
-
-```{note}
-In signal processing, it is common to write a sampled (discrete) signal as a small letter $s_i$ and the Fourier coefficients with capitals $S_k$. Since we also use capitals to indicate that $S$ is a random variable, we describe the DFT here for a realization $s_i$ of $S_i$, and use the notation $F_S(k)$ for the Fourier coefficients.
-```
-
-Conversely, the inverse discrete Fourier transform (IDFT) of the PSD is the ACF, so
-
-$$\text{IDFT}(F_{S}(k))=\hat{c}_{\tau}, \hspace{35px} \tau = 1,...,m \hspace{5px}\text{and}\hspace{5px} k = 1,...,m$$
-
-```{figure} ./figs/ACF_PSD.png
----
-height: 300px
-name: ACF_PSD
----
-Time series data, autocovariance and its power spectral density plots of white noise above and colored noise (not purely random) below.
-```
-
-The PSD explains how the power (variance) of the signal is distributed over different frequencies. The PSD of a pure sine wave is flat except at its constituent frequency.
-Purey random noise (i.e., white noise) has a flat power, indicating that all frequencies have identical contribution in making the variance of the signal. This is however not the case for time-correlated noise because different frequencies have different power values in making the total signal variability.
-
diff --git a/book/time_series/ar.ipynb b/book/time_series/ar.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..18a5416cdaf0843e799a78bc79ee45c5b9d60f64
--- /dev/null
+++ b/book/time_series/ar.ipynb
@@ -0,0 +1,271 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(AR)=\n",
+    "# AR process\n",
+    "The code on this page can be used interactively: click {fa}`rocket` --> {guilabel}`Live Code` in the top right corner, then wait until the message {guilabel}`Python interaction ready!` appears.\n",
+    "\n",
+    "The main goal is to introduce the AutoRegressive (AR) model to describe a **stationary stochastic process**. Hence the AR model can be applied on time series where e.g. trend and seasonality are not present / removed, and only noise remains, or after applying other methods [to obtain a stationary time series](stationarize).\n",
+    "\n",
+    "## Process definition\n",
+    "\n",
+    "In an AR model, we forecast the variable of interest using a linear combination of its past values. A zero mean AR process of orders $p$ can be written as follows:\n",
+    "\n",
+    "$$S_t = \\overbrace{\\phi_1S_{t-1}+...+\\phi_pS_{t-p}}^{\\text{AR process}} + e_t $$ \n",
+    "\n",
+    "or as\n",
+    "\n",
+    "$$S_t = \\sum_{i=1}^p \\phi_iS_{t-i}+e_t$$\n",
+    "\n",
+    "Each observation is made up of a **random error** $e_t$ at that epoch, a linear combination of **past observations**. The errors $e_t$  are uncorrelated purely random noise process, known also as white noise. We note the process should still be stationary, satisfying\n",
+    "\n",
+    "$$\\mathbb{E}(S_t)=0, \\hspace{20px} \\mathbb{D}(S_t)=\\sigma^2,\\quad \\forall t$$\n",
+    "\n",
+    "This indicates that parts of the total variability of the process come from the signal and noise of past epochs, and only a (small) portion belongs to the noise of that epoch (denoted as $e_t$). To have a better understanding of the process itself, we consider two special cases, $q=0$ and $p=0$.\n",
+    "\n",
+    "### First-order AR(1) process\n",
+    "\n",
+    "We will just focus on explaining $p=1$, i.e. the AR(1) process. A **zero-mean first order autoregressive** process can be written as follows\n",
+    "\n",
+    "$$ S_t = \\phi S_{t-1}+e_t, \\hspace{20px} -1\\leq\\phi<1, \\hspace{20px} t=2,...,m $$\n",
+    "\n",
+    "where $e_t$ is an i.i.d. noise process, e.g. distributed as $e_t\\sim N(0,\\sigma_{e}^2)$. See later the definition of $\\sigma_{e}^2$.\n",
+    "\n",
+    ":::{card} Exercise\n",
+    "\n",
+    "In a zero-mean first order autoregressive process, abbreviated as AR(1), we have $m=3$ observations, $\\phi=0.8$, and the generated white noise errors are $e = [e_1,\\, e_2,\\, e_3]^T=[1,\\, 2,\\, -1]^T$. What is the generated AR(1) process $S = [S_1,\\, S_2,\\, S_3]^T$?\n",
+    "\n",
+    "a. $S = \\begin{bmatrix}1 & 2.8 & 1.24\\end{bmatrix}^T$  \n",
+    "b. $S = \\begin{bmatrix} 0 & 2 & 0.6 \\end{bmatrix}^T$  \n",
+    "c. $S = \\begin{bmatrix} 1 & 2 & -1 \\end{bmatrix}^T$  \n",
+    "\n",
+    "```{admonition} Solution\n",
+    ":class: tip, dropdown\n",
+    "\n",
+    "The correct answer is **a**. The AR(1) process can be initialized as $S_1=e_1=1$. The next values can be obtained through:\n",
+    "\n",
+    "$$ S_t = \\phi S_{t-1} + e_t $$\n",
+    "\n",
+    "Giving $S_2=0.8 S_1 + e_2 = 0.8\\cdot 1 + 2 = 2.8$ and $S_3=0.8 S_2 + e_3 = 0.8\\cdot 2.8 - 1= 1.24$, so we have:\n",
+    "\n",
+    "$$S = \\begin{bmatrix}1 & 2.8 & 1.24\\end{bmatrix}^T $$\n",
+    "\n",
+    "```\n",
+    ":::\n",
+    "\n",
+    "### Formulation\n",
+    "\n",
+    "Initializing $S_1=e_1$, with $\\mathbb{E}(S_1)=\\mathbb{E}(e_1)=0$ and $\\mathbb{D}(S_1)=\\mathbb{D}(e_1)=\\sigma^2$. Following this, multiple applications of the above \"autoregressive\" formula ($S_t = \\phi S_{t-1} + e_t$) gives:\n",
+    "\n",
+    "$$ \\begin{align*}\n",
+    "S_1&=e_1\\\\ \n",
+    "S_2&=\\phi S_1+e_2\\\\ \n",
+    "S_3 &= \\phi S_2+e_3 = \\phi^2S_1+\\phi e_2+e_3\\\\ \n",
+    "&\\vdots\\\\ \n",
+    "S_m &= \\phi S_{m-1} + e_m = \\phi^{m-1}S_1+\\phi^{m-2}e_2+...+\\phi e_{m-1}+e_m\n",
+    "\\end{align*} $$\n",
+    "\n",
+    "of which we still have (in order to impose the *stationarity*):\n",
+    "\n",
+    "$$\\mathbb{E}(S_t)=0 \\hspace{5px}\\text{and}\\hspace{5px} \\mathbb{D}(S_t)=\\sigma^2, \\hspace{10px} t=1,...,m$$\n",
+    "\n",
+    "All the error components, $e_t$, are uncorrelated such that $Cov(e_t,e_{t+\\tau})=0$ if $\\tau \\neq 0$, and with variance $\\sigma_{e}^2$ which still needs to be determined.\n",
+    "\n",
+    "### Autocovariance\n",
+    "\n",
+    "The mean of the process is zero and, therefore:\n",
+    "\n",
+    "$$\\mathbb{E}(S_t) = \\mathbb{E}(\\phi S_{t-1}+e_t) = \\phi\\mathbb{E}(S_{t-1})+\\mathbb{E}(e_t) = 0$$\n",
+    "\n",
+    "The variance of the process should remain constant as:\n",
+    "\n",
+    "$$\\mathbb{D}(S_t) = \\mathbb{D}(\\phi S_{t-1} +e_t) \\Leftrightarrow \\sigma^2=\\phi^2\\sigma^2+\\sigma_{e}^2, \\hspace{10px} t\\geq 2$$\n",
+    "\n",
+    "resulting in\n",
+    "\n",
+    "$$\\sigma_{e}^2 = \\sigma^2 (1-\\phi^2)$$\n",
+    "\n",
+    "indicating that $\\sigma_{e}^2$ is smaller than $\\sigma^2$.\n",
+    "\n",
+    "The autocovariance (covariance between $S_t$ and $S_{t+\\tau}$) is\n",
+    "\n",
+    "$$ \\begin{align*}\n",
+    "c_{\\tau}&=\\mathbb{E}(S_t S_{t+\\tau})-\\mu^2 =\\mathbb{E}(S_t S_{t+\\tau})\\\\\n",
+    "&= \\mathbb{E}(S_t(\\phi^\\tau S_t + \\phi^{\\tau-1} e_{t+1}+...)) = \\phi^\\tau\\mathbb{E}(S_t^2)=\\sigma^2\\phi^\\tau\n",
+    "\\end{align*} $$\n",
+    "\n",
+    "In the derivation above we used that:\n",
+    "\n",
+    "$$ \\begin{align*}\n",
+    "S_{t+\\tau}=\\phi^\\tau S_t + \\phi^{\\tau-1} e_{t+1}+...+e_{t+\\tau}\n",
+    "\\end{align*} $$\n",
+    "\n",
+    "and the fact that $S_t$ and $e_{t+\\tau}$ are uncorrelated for $\\tau \\neq 0$.\n",
+    "\n",
+    "```{admonition} Derivation (optional)\n",
+    ":class: tip, dropdown\n",
+    "\n",
+    "$$ \\begin{align*}\n",
+    "S_{t+\\tau}&= \\phi^{t+\\tau-1}S_1 + \\phi^{t+\\tau-2}e_2+...+ \\phi^{\\tau} e_{t}+ \\phi^{\\tau-1} e_{t+1}+...+e_{t+\\tau}\\\\\n",
+    "&= \\phi^{\\tau} \\left(\\phi^{t-1}S_1 + \\phi^{t-2}e_2+...+  e_{t}\\right)+ \\phi^{\\tau-1} e_{t+1}+...+e_{t+\\tau}\\\\\n",
+    "&=\\phi^\\tau S_t + \\phi^{\\tau-1} e_{t+1}+...+e_{t+\\tau}\n",
+    "\\end{align*} $$\n",
+    "\n",
+    "```\n",
+    "\n",
+    "### Model structure of AR(1)\n",
+    "\n",
+    "$$\\mathbb{E}(S) = \\mathbb{E}\\begin{bmatrix}S_1\\\\ S_2\\\\ \\vdots\\\\ S_m\\end{bmatrix} = \\begin{bmatrix}0\\\\ 0\\\\ \\vdots\\\\ 0\\end{bmatrix}, \\hspace{15px} \\mathbb{D}(S)=\\Sigma_{S}=\\sigma^2 \\begin{bmatrix}1&\\phi&...&\\phi^{m-1}\\\\ \\phi&1&...&\\phi^{m-2}\\\\ \\vdots&\\vdots&\\ddots&\\vdots\\\\ \\phi^{m-1}&\\phi^{m-2}&...&1\\end{bmatrix}$$\n",
+    "\n",
+    "* Autocovariance function $\\implies$ $c_{\\tau}=\\sigma^2\\phi^\\tau$\n",
+    "* Normalized autocovariance function (ACF) $\\implies$ $\\rho_\\tau=c_{\\tau}/c_0=\\phi^\\tau$\n",
+    "* Larger value of $\\phi$ indicates a long-memory random process\n",
+    "* If $\\phi=0$, this is called *purely random process* (white noise)\n",
+    "* ACF is even, $c_{\\tau}=c_{-\\tau}=c_{|\\tau|}$ and so is $\\rho_{\\tau}=\\rho_{-\\tau}=\\rho_{|\\tau|}$\n",
+    "\n",
+    "Later in this section we will see how the coefficient $\\phi$ can be estimated.\n",
+    "\n",
+    "## Simulated example\n",
+    "\n",
+    "If you have run the python code on this page, an interactive plot will be displayed below. You can change the value of $\\phi$ and the number of observations $m$ to see how the AR(1) process changes. At the start, the process is initialized with $\\phi = 0.8$. Try moving the slider and see the response of the ACF; pay special attention when $\\phi=0$ and when $\\phi$ becomes negative. \n",
+    "\n",
+    "Lastly, focus on the case where $\\phi=1$ and $\\phi=-1$. What do you observe? You will notice that the function will \"explode\". This makes intuitive sense, since the effect of the previous epoch is not dampened, but rather amplified. This also means that the process is not stationary anymore. So, the AR(1) process is stationary if $|\\phi|<1$.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "auto-execute-page",
+     "thebe-remove-input-init"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3cffa76164d46349e929a2a83006563",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.8, description='Phi:', max=1.0, min=-1.0), Output()), _dom_classes=(…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from statsmodels.graphics.tsaplots import plot_acf\n",
+    "import ipywidgets as widgets\n",
+    "from ipywidgets import interact\n",
+    "\n",
+    "\n",
+    "def acfplot(phi=0.8):\n",
+    "    # Parameters for the AR(1) process\n",
+    "\n",
+    "    sigma = 1          # Standard deviation of the noise\n",
+    "    n = 500            # Length of the time series\n",
+    "\n",
+    "    # Initialize the process\n",
+    "    np.random.seed(42)  # For reproducibility\n",
+    "    X = np.zeros(n)\n",
+    "    X[0] = np.random.normal(0, sigma)  # Initial value\n",
+    "\n",
+    "    # Generate the AR(1) process and noise series\n",
+    "    noise = np.random.normal(0, sigma, n)  # Pre-generate noise for the second plot\n",
+    "    for t in range(1, n):\n",
+    "        X[t] = phi * X[t-1] + noise[t]  # Use pre-generated noise\n",
+    "\n",
+    "    # Create the 2x1 subplot\n",
+    "    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 6), sharex=False)\n",
+    "\n",
+    "    # Plot the AR(1) process in the first subplot\n",
+    "    ax1.plot(X, label=f\"AR(1) Process with φ={round(phi, 2)}\")\n",
+    "    ax1.set_ylabel(\"Value\")\n",
+    "    ax1.set_title(\"Simulated AR(1) Process\")\n",
+    "    ax1.set_ylim(-4, 4)\n",
+    "    ax1.legend()\n",
+    "    ax1.grid(True)\n",
+    "\n",
+    "    # Plot the white noise in the second subplot\n",
+    "    lags = 20\n",
+    "    plot_acf(X, ax=ax2, lags=lags, title=\"ACF of White Noise\")\n",
+    "    ax2.set_xlabel(\"Time\")\n",
+    "    ax2.set_ylabel(\"ACF Value\")\n",
+    "    ax2.set_title(\"ACF of AR(1) Process\")\n",
+    "    ax2.set_xlim(-0.5, lags)\n",
+    "    ax2.grid(True)\n",
+    "\n",
+    "    # Display the plot\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "interact(acfplot, phi=widgets.FloatSlider(value=0.8, min=-1, max=1.0, step=0.1, description='Phi:'));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Estimation of coefficients of AR process\n",
+    "\n",
+    "If the values of $p$ and of the AR($p$) process are known, the question is: **how can we estimate the coefficients $\\phi_1,...,\\phi_p$**\n",
+    "\n",
+    "Here, we only elaborate on AR(1) using best linear unbiased estimation (BLUE) to estimate $\\phi_1$. The method can be generalized to estimate the parameters of an AR($p$) process.\n",
+    "\n",
+    "**Example: Parameter estimation of AR(1)**\n",
+    "\n",
+    "The AR(1) process is of the form\n",
+    "\n",
+    "$$S_t=\\phi_1 S_{t-1}+e_t$$\n",
+    "\n",
+    "In order to estimate the $\\phi_i$ we can set up the following linear model of observation equations (starting from $t=2$):\n",
+    "\n",
+    "$$\\begin{bmatrix}S_2 \\\\ S_3 \\\\ \\vdots \\\\ S_m \\end{bmatrix} = \\begin{bmatrix}S_1 \\\\S_2 \\\\ \\vdots\\\\ S_{m-1} \\end{bmatrix}\\begin{bmatrix}\\phi_1 \\end{bmatrix} + \\begin{bmatrix}e_{2} \\\\ e_{3}\\\\ \\vdots \\\\ e_{m} \\end{bmatrix}$$\n",
+    "\n",
+    "The BLUE estimator of $\\phi$ is given by:\n",
+    "\n",
+    "$$\\hat{\\phi}=(\\mathrm{A}^T\\mathrm{A})^{-1}\\mathrm{A}^TS$$\n",
+    "\n",
+    "Where $\\mathrm{A}=\\begin{bmatrix}S_1 & S_2 & \\cdots & S_{m-1}\\end{bmatrix}^T$ and $S=\\begin{bmatrix}S_2 & S_3 & \\cdots & S_m\\end{bmatrix}^T$.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "TAMude",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/book/time_series/ar_exercise.ipynb b/book/time_series/ar_exercise.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d9044e82542889e38b4f151176752bc1e49e9e3c
--- /dev/null
+++ b/book/time_series/ar_exercise.ipynb
@@ -0,0 +1,287 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fit AR(p) model\n",
+    "In this exercise, we will fit an AR(p) model to a time series.The timeseries is already stationary and we will use two different methods to choose the number of lag (p) to include. First we will start by loading and displaying the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#load data\n",
+    "data = np.loadtxt('generated_time_series.csv', delimiter=',', skiprows=1)\n",
+    "\n",
+    "#plot data\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "plt.plot(data)\n",
+    "plt.xlabel('Time')\n",
+    "plt.ylabel('Value')\n",
+    "plt.title('Generated stationary Time Series')\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we can use `plot_acf` to plot the autocorrelation function of the time series. From the resulting plot we can see that that there is autocorrelation present in the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Plot the ACF of the time series\n",
+    "fig, ax = plt.subplots(figsize=(12, 4))\n",
+    "plot_acf(data, lags=40, ax=ax)\n",
+    "ax.grid(True);\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "source": [
+    "A good indication for the number of lags to include in the AR model is the number of lags that are significant according to the PACF (partial autocorrelation function). We can use the `plot_pacf` function to plot the PACF of the time series. From the resulting plot we can see that the first lag and the second lag are significant. This suggests that we should use an AR(2) model.\n",
+    "\n",
+    "```{note}\n",
+    "The PACF is similar to the ACF, but it is the correlation between the time series and a lagged version of itself that is not explained by correlations at all lower-order lags. In other words, the PACF is the ACF with the linear dependence of the earlier lags removed.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the PACF of the time series\n",
+    "fig, ax = plt.subplots(figsize=(12, 4))\n",
+    "plot_pacf(data, lags=40, ax=ax)\n",
+    "ax.grid(True);\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make sure that we have selected the correct number of lags, we can test for significance using the generalized likelihood ratio test (GLRT). For a detailed explanation of the GLRT, see the chapter on observation theory. We will apply the GLRT to the AR(1) vs AR(2) and AR(2) vs AR(3) models. \n",
+    "\n",
+    "The AR(1), AR(2) and AR(3) models are defined as follows:\n",
+    "\n",
+    "AR(1): $S_t = \\phi_1 S_{t-1} + \\epsilon_t$\n",
+    "\n",
+    "AR(2): $S_t = \\phi_1 S_{t-1} + \\phi_2 S_{t-2} + \\epsilon_t$\n",
+    "\n",
+    "AR(3): $S_t = \\phi_1 S_{t-1} + \\phi_2 S_{t-2} + \\phi_3 S_{t-3} + \\epsilon_t$\n",
+    "\n",
+    "Since we need $S_{t-3}$ we will have $y = S[3:]$ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AR(1) vs AR(2) test statistic: 65.46386398456401 Critical value: 3.841458820694124\n",
+      "Reject AR(1) in favor of AR(2)\n",
+      "AR(2) vs AR(3) test statistic: 3.5318291727430835 Critical value: 3.841458820694124\n",
+      "Fail to reject AR(2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.stats import chi2\n",
+    "\n",
+    "y = data[3:]\n",
+    "s1 = data[2:-1]\n",
+    "s2 = data[1:-2]\n",
+    "s3 = data[:-3]\n",
+    "n = len(y)\n",
+    "\n",
+    "# AR(1) model\n",
+    "X1 = np.column_stack((s1)).T\n",
+    "phi_ar1 = np.linalg.inv(X1.T @ X1) @ (X1.T @ y)\n",
+    "e1 = y - X1 @ phi_ar1\n",
+    "rss1 = e1.T @ e1\n",
+    "\n",
+    "# AR(2) model\n",
+    "X2 = np.column_stack((s1, s2))\n",
+    "phi_ar2 = np.linalg.inv(X2.T @ X2) @ X2.T @ y\n",
+    "e2 = y - X2 @ phi_ar2\n",
+    "rss2 = e2.T @ e2\n",
+    "\n",
+    "# AR(3) model\n",
+    "X3 = np.column_stack((s1, s2, s3))\n",
+    "phi_ar3 = np.linalg.inv(X3.T @ X3) @ X3.T @ y\n",
+    "e3 = y - X3 @ phi_ar3\n",
+    "rss3 = e3.T @ e3\n",
+    "\n",
+    "# test ar(1) vs ar(2) using log likelihood ratio test\n",
+    "dof = 1\n",
+    "crit = chi2.ppf(0.95, dof)\n",
+    "test_stat = n * np.log(rss1 / rss2)\n",
+    "print('AR(1) vs AR(2) test statistic:', test_stat, 'Critical value:', crit)\n",
+    "\n",
+    "if test_stat > crit:\n",
+    "    print('Reject AR(1) in favor of AR(2)')\n",
+    "else:\n",
+    "    print('Fail to reject AR(1)')\n",
+    "\n",
+    "# test ar(2) vs ar(3) using log likelihood ratio test\n",
+    "dof = 1\n",
+    "crit = chi2.ppf(0.95, dof)\n",
+    "test_stat = n * np.log(rss2 / rss3)\n",
+    "print('AR(2) vs AR(3) test statistic:', test_stat, 'Critical value:', crit)\n",
+    "\n",
+    "if test_stat > crit:\n",
+    "    print('Reject AR(2) in favor of AR(3)')\n",
+    "else:\n",
+    "    print('Fail to reject AR(2)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the GLRT results we can see that an AR(2) model is the best fit for the data. Finally we will fit the AR(2) model to the data and plot the residuals. The residuals should be white noise, which is confirmed by the ACF plots of the residuals. Plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AR(2) Coefficients:\n",
+      "Phi_1 =  0.4955 +/- 0.0602\n",
+      "Phi_2 =  0.2527 +/- 0.0602\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the residuals\n",
+    "\n",
+    "fig, ax = plt.subplots(2,1,figsize=(12, 6))\n",
+    "ax[0].plot(e2)\n",
+    "ax[0].set_title('AR(2) Residuals')\n",
+    "ax[0].grid(True)\n",
+    "\n",
+    "plot_acf(e2, lags=40, ax=ax[1])\n",
+    "ax[1].grid(True)\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "\n",
+    "\n",
+    "sigma_e2 = np.std(e2)\n",
+    "var_phi2 = sigma_e2**2 * np.linalg.inv(X2.T @ X2)\n",
+    "sigma_phi2 = np.sqrt(np.diag(var_phi2))\n",
+    "\n",
+    "# print the AR(2) coefficients with confidence intervals\n",
+    "print('AR(2) Coefficients:')\n",
+    "print('Phi_1 = ', round(phi_ar2[0], 4), '+/-', round(1.96 * sigma_phi2[0], 4))\n",
+    "print('Phi_2 = ', round(phi_ar2[1], 4), '+/-', round(1.96 * sigma_phi2[1], 4))\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "TAMude",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/book/time_series/arma.md b/book/time_series/arma.md
deleted file mode 100644
index 51133d3d8eb8dd53f1e06ff361467a7dd8b8177f..0000000000000000000000000000000000000000
--- a/book/time_series/arma.md
+++ /dev/null
@@ -1,362 +0,0 @@
-(ARMA)=
-# ARMA process
-
-The main goal is to introduce the AutoRegressive Moving Average (ARMA) model to describe a **stationary stochastic process**. Hence the ARMA model can be applied on time series where e.g. trend and seasonality are not present / removed, and only noise remains, or after applying other methods [to obtain a stationary time series](stationarize).
-
-## Process definition
-
-In an ARMA model, we forecast the variable of interest using a linear combination of its past values plus the current and past errors. A zero mean ARMA process of orders $p$ and $q$ can be written as follows:
-
-$$S_t = \overbrace{\beta_1S_{t-1}+...+\beta_pS_{t-p}}^{\text{AR process}} + e_t + \overbrace{\theta_1 e_{t-1}+...+\theta_q e_{t-q}}^{\text{MA process}}$$ 
-
-or as
-
-$$S_t = \sum_{i=1}^p \beta_iS_{t-i}+e_t+\sum_{i=1}^q \theta_i e_{t-i}$$
-
-Each observation is made up of a **random error** $e_t$ at that epoch, a linear combination of **past observations**, and a linear combination of **past errors**. The errors $e_t$  are uncorrelated purely random noise process, known also as white noise. We note the process should still be stationary, satisfying
-
-$$\mathbb{E}(S_t)=0, \hspace{20px} \mathbb{D}(S_t)=\sigma^2,\quad \forall t$$
-
-This indicates that parts of the total variability of the process come from the signal and noise of past epochs, and only a (small) portion belongs to the noise of that epoch (denoted as $e_t$). To have a better understanding of the process itself, we consider two special cases, $q=0$ and $p=0$.
-
-### Special case 1: ARMA$(p,0) = $ AR$(p)$
-
-The first special case we are going to study considers $q=0$. A zero mean $p$-order autoregressive (AR) random process, abbreviated to ARMA($p,0$) = AR($p$), can be written as follows
-
-$$S_t = \beta_1S_{t-1}+...+\beta_pS_{t-p} + e_t=S_t = \sum_{i=1}^p \beta_iS_{t-i}+e_t$$
-
-#### First-order AR(1) process
-
-We will just focus on explaining $p=1$, i.e. the AR(1) process. A **zero-mean first order autoregressive** process can be written as follows
-
-$$S_t = \beta S_{t-1}+e_t, \hspace{20px} -1\leq\beta<1, \hspace{20px} t=2,...,m$$
-
-where $e_t$ is an i.i.d. noise process, e.g. distributed as $e_t\sim N(0,\sigma_{e}^2)$. See later the definition of $\sigma_{e}^2$.
-
-:::{card} Exercise
-
-In a zero-mean first order autoregressive process, abbreviated as AR(1), we have $m=3$ observations, $\beta=0.8$, and the generated white noise errors are $e = [e_1,\, e_2,\, e_3]^T=[1,\, 2,\, -1]^T$. What is the generated AR(1) process $S = [S_1,\, S_2,\, S_3]^T$?
-
-a. $S = \begin{bmatrix}1 & 2.8 & 1.24\end{bmatrix}^T$  
-b. $S = \begin{bmatrix} 0 & 2 & 0.6 \end{bmatrix}^T$  
-c. $S = \begin{bmatrix} 1 & 2 & -1 \end{bmatrix}^T$  
-
-```{admonition} Solution
-:class: tip, dropdown
-
-The correct answer is **a**. The AR(1) process can be initialized as $S_1=e_1=1$. The next values can be obtained through:
-
-$$
-S_t = \beta S_{t-1} + e_t
-$$
-
-Giving $S_2=0.8 S_1 + e_2 = 0.8\cdot 1 + 2 = 2.8$ and $S_3=0.8 S_2 + e_3 = 0.8\cdot 2.8 - 1= 1.24$, so we have:
-
-$$
-S = 
-\begin{bmatrix}1 & 2.8 & 1.24\end{bmatrix}^T 
-$$
-
-```
-:::
-
-**Formulation**
-
-Initializing $S_1=e_1$, with $\mathbb{E}(S_1)=\mathbb{E}(e_1)=0$ and $\mathbb{D}(S_1)=\mathbb{D}(e_1)=\sigma^2$. Following this, multiple applications of the above "autoregressive" formula ($S_t = \beta S_{t-1} + e_t$) gives:
-
-$$
-\begin{align*}
-S_1&=e_1\\ 
-S_2&=\beta S_1+e_2\\ 
-S_3 &= \beta S_2+e_3 = \beta^2S_1+\beta e_2+e_3\\ 
-&\vdots\\ 
-S_m &= \beta S_{m-1} + e_m = \beta^{m-1}S_1+\beta^{m-2}e_2+...+\beta e_{m-1}+e_m
-\end{align*}
-$$
-
-of which we still have (in order to impose the *stationarity*):
-
-$$\mathbb{E}(S_t)=0 \hspace{5px}\text{and}\hspace{5px} \mathbb{D}(S_t)=\sigma^2, \hspace{10px} t=1,...,m$$
-
-All the error components, $e_t$, are uncorrelated such that $Cov(e_t,e_{t+\tau})=0$ if $\tau \neq 0$, and with variance $\sigma_{e}^2$ which still needs to be determined.
-
-**Autocovariance**
-
-The mean of the process is zero and, therefore:
-
-$$\mathbb{E}(S_t) = \mathbb{E}(\beta S_{t-1}+e_t) = \beta\mathbb{E}(S_{t-1})+\mathbb{E}(e_t) = 0$$
-
-The variance of the process should remain constant as:
-
-$$\mathbb{D}(S_t) = \mathbb{D}(\beta S_{t-1} +e_t) \Leftrightarrow \sigma^2=\beta^2\sigma^2+\sigma_{e}^2, \hspace{10px} t\geq 2$$
-
-resulting in
-
-$$\sigma_{e}^2 = \sigma^2 (1-\beta^2)$$
-
-indicating that $\sigma_{e}^2$ is smaller than $\sigma^2$.
-
-The autocovariance (covariance between $S_t$ and $S_{t+\tau}$) is
-
-$$
-\begin{align*}
-c_{\tau}&=\mathbb{E}(S_t S_{t+\tau})-\mu^2 =\mathbb{E}(S_t S_{t+\tau})\\
-&= \mathbb{E}(S_t(\beta^\tau S_t + \beta^{\tau-1} e_{t+1}+...)) = \beta^\tau\mathbb{E}(S_t^2)=\sigma^2\beta^\tau
-\end{align*}$$
-
-In the derivation above we used that:
-
-$$
-\begin{align*}
-S_{t+\tau}=\beta^\tau S_t + \beta^{\tau-1} e_{t+1}+...+e_{t+\tau}
-\end{align*}
-$$
-
-and the fact that $S_t$ and $e_{t+\tau}$ are uncorrelated for $\tau \neq 0$.
-
-```{admonition} Derivation (optional)
-:class: tip, dropdown
-
-$$
-\begin{align*}
-S_{t+\tau}&= \beta^{t+\tau-1}S_1 + \beta^{t+\tau-2}e_2+...+ \beta^{\tau} e_{t}+ \beta^{\tau-1} e_{t+1}+...+e_{t+\tau}\\
-&= \beta^{\tau} \left(\beta^{t-1}S_1 + \beta^{t-2}e_2+...+  e_{t}\right)+ \beta^{\tau-1} e_{t+1}+...+e_{t+\tau}\\
-&=\beta^\tau S_t + \beta^{\tau-1} e_{t+1}+...+e_{t+\tau}
-\end{align*}
-$$
-
-```
-
-**Model structure of AR(1)**
-
-$$\mathbb{E}(S) = \mathbb{E}\begin{bmatrix}S_1\\ S_2\\ \vdots\\ S_m\end{bmatrix} = \begin{bmatrix}0\\ 0\\ \vdots\\ 0\end{bmatrix}, \hspace{15px} \mathbb{D}(S)=\Sigma_{S}=\sigma^2 \begin{bmatrix}1&\beta&...&\beta^{m-1}\\ \beta&1&...&\beta^{m-2}\\ \vdots&\vdots&\ddots&\vdots\\ \beta^{m-1}&\beta^{m-2}&...&1\end{bmatrix}$$
-
-* Autocovariance function $\implies$ $c_{\tau}=\sigma^2\beta^\tau$
-* Normalized autocovariance function (ACF) $\implies$ $\rho_\tau=c_{\tau}/c_0=\beta^\tau$
-* Larger value of $\beta$ indicates a long-memory random process
-* If $\beta=0$, this is called *purely random process* (white noise)
-* ACF is even, $c_{\tau}=c_{-\tau}=c_{|\tau|}$ and so is $\rho_{\tau}=\rho_{-\tau}=\rho_{|\tau|}$
-
-Later in this section we will see how the coefficient $\beta$ can be estimated.
-
-**Simulated example**
-
-A time series has been simulated to have a standard normal distribution, $S_i \sim N(0,1)$. This indicates that the first entry is $S_1 \sim \text{N}(0,1)$ and the remaining errors are $e_i \sim N(0,1-\beta^2)$, $i=2,...,m=1000$. The time series is shown in {numref}`ar1example`. Time correlation can be visually seen in the data.
-
-The normalized ACF shows the temporal correlation, $\rho_{\tau}=\beta^{\tau}$, where $\tau=0,1,2,...,m-1$.
-
-```{figure} ./figs/ar1example.png
-:name: ar1example
-:width: 600px
-:align: center
-
-Left: time series for $\beta =0.7$ and $\beta =-0.7$. Right: corresponding normalized autocovariance functions.
-```
-
-### Special case 2: ARMA$(0,q) = $ MA$(q)$
-
-A zero mean $q$-order moving average random process, abbreviated to ARMA(0,q) = MA(q), can be written as follows
-
-$$S_t=\theta_1 e_{t-1}+...+\theta_q e_{t-q}+e_t$$
-
-or
-
-$$S_t=\sum_{i=1}^q \theta_i e_{t-i} + e_t$$
-
-#### First-order MA(1) process
-
-Here we will just focus on the case $q=1$, i.e. MA(1). A **zero-mean first order moving average process** can be written as:
-
-$$S_t = \theta e_{t-1} + e_t, \hspace{10px} -1\leq\theta<1 \hspace{10px} t=2,...,m$$
-
-where $e_t$ is an i.i.d. noise process (white noise), e.g. distributed as $e_t\sim N(0,\sigma_{e}^2)$
-
-**Formulation**
-
-Initializing $S_1=e_1$, with $\mathbb{E}(S_1)=\mathbb{E}(e_1)=0$, $Var(S_1)=\sigma^2$ and $Var(e_i)=\sigma_{e}^2$ for $i=2,\dots,m$. Following this, multiple applications of the above "moving average" formula  gives:
-
-$$\begin{align*}S_1&=e_1\\ S_2&=\theta e_1+e_2\\ S_3 &= \theta e_2+e_3\\ &\vdots\\ S_m &= \theta e_{m-1} + e_m\end{align*}$$
-
-of which we still have (in order to impose the *stationarity*):
-
-$$\mathbb{E}(S_t)=0 \hspace{5px}\text{and}\hspace{5px} \mathbb{D}(S_t)=\sigma^2, \hspace{10px} t=1,...,m$$
-
-All the error components, $e_t$, are uncorrelated such that $Cov(e_t,e_{t+\tau})=0$ if $\tau\neq 0$, and the variance is $\sigma_e^2$.
-
-**Autocovariance**
-
-The mean of the process is zero and, therefore:
-
-$$\mathbb{E}(S_t) = \mathbb{E}(\theta e_{t-1}+e_t) =\theta\mathbb{E}(e_{t-1})+\mathbb{E}(e_t) = 0$$
-
-The variance of the process should remain constant as:
-
-$$\mathbb{D}(S_t) = \mathbb{D}(\theta e_{t-1}+e_t) \Leftrightarrow  \sigma^2=\theta^2\sigma_e^2+\sigma_e^2, \hspace{10px} t\geq 2$$
-
-resulting in
-
-$$ \sigma_e^2 = \frac{\sigma^2}{1+\theta^2}$$
-
-indicaating that $\sigma_e^2$ is smaller than $\sigma^2$
-
-The autocovariance is
-
-$$c_1=Cov(S_t, S_{t+1}) = \sigma_e^2\theta\\ c_{-1}=Cov(S_t, S_{t-1}) =  \sigma_e^2\theta$$
-
-and
-
-$$c_{\tau}=Cov(S_t,S_{t+\tau}) = 0, \hspace{10px}\text{for}\hspace{5px}\tau\geq 2$$
-
-The normalized auto-covariance function (ACF) follows:
-
-$$\rho_{\tau}=\frac{c_{\tau}}{\sigma^2}=\begin{cases}\frac{\theta}{1+\theta^2}, \hspace{5px}&\text{if}\hspace{5px}\tau=1\\ 0, \hspace{5px}&\text{if}\hspace{5px}\tau\neq 1\end{cases}
-$$
-
-**Model structure**
-
-$$\mathbb{E}(S) = \mathbb{E}\begin{bmatrix}S_1\\ S_2\\ \vdots\\ S_m\end{bmatrix} = \begin{bmatrix}0\\ 0\\ \vdots\\ 0\end{bmatrix}, \hspace{15px} \mathbb{D}(S)=\Sigma_{S}=\sigma^2\begin{bmatrix}1&\rho_1&0&\dots&0\\ \rho_1&1&\rho_1& &\\ 0&\rho_1&1&\ddots&0\\ \vdots& &\ddots&\ddots&\rho_1\\ 0&\dots&0&\rho_1&1\end{bmatrix}$$
-
-In summmary: 
-
-* Autocovariance function $\implies$ $c_{\tau}=\begin{cases}\frac{\sigma^2\theta}{1+\theta^2}, \hspace{5px}&\text{if}\hspace{5px}\tau=1\\ 0, \hspace{5px}&\text{if}\hspace{5px}\tau>1\end{cases}$
-
-* Normalized auto-covariance function (ACF) $\implies$ $\rho_\tau=\begin{cases}\frac{\theta}{1+\theta^2}, \hspace{5px}&\text{if}\hspace{5px}\tau=1\\ 0, \hspace{5px}&\text{if}\hspace{5px}\tau\neq 1\end{cases}$
-
-* ACF is even, $c_{\tau}=c_{-\tau}=c_{|\tau|}$ and so is $\rho_{\tau}=\rho_{-\tau}=\rho_{|\tau|}$
-
-**Simulated example**
-
-A time series has been simulated to have a standard normal distribution, $e_i \sim \text{N}(0,1)$. This indicates that the entries of $S$ have $S_i \sim \text{N}(0,1+\theta^2)$, $i=1,...,m=1000$, where the variance of the noise process is $\sigma^2 = 1+\theta^2$. In fact, $\sigma_{e_t}=1$, but not the random process MA(1) in total. The time series is shown in {numref}`ma1ex`. 
-
-The normalized ACF shows the temporal correlation, $\rho_{\tau}=\frac{\theta}{1+\theta^2}$, if $\tau=1$, and $\rho_{\tau}=0$ if $\tau>1$.
-
-MMMMM should delete the equation in the right panels!
-
-```{figure} ./figs/ma1ex.png
-:name: ma1ex
-:width: 600px
-:align: center
-
-Left: time series for $\beta =0.9$ and $\beta =-0.9$. Right: corresponding normalized autocovariance functions.
-```
-
-## Estimation of coefficients of ARMA process
-
-If the values of $p$ and $q$ of the ARMA($p,q$) process are known, the question is: **how can we estimate the coefficients $\beta_1,...,\beta_p$ and $\theta_1,...,\theta_q$?**
-
-Here, we only elaborate on AR(2) = ARMA(2,0) using best linear unbiased estimation (BLUE) to estimate $\beta_1$ and $\beta_2$. The method can be generalized to estimate the parameters of an ARMA($p,q$) process.
-
-**Example: Parameter estimation of AR(2)**
-
-The AR(2) process is of the form
-
-$$S_t=\beta_1 S_{t-1}+\beta_2 S_{t-2}+e_t$$
-
-In order to esitimate the $\beta_i$ we can set up the following linear model of observation equations (starting from $t=3$):
-
-$$\begin{bmatrix}S_3 \\ S_4 \\ \vdots \\ S_m \end{bmatrix} = \begin{bmatrix}S_2 & S_1 \\S_3 & S_2\\ \vdots & \vdots\\ S_{m-1}&S_{m-2} \end{bmatrix}\begin{bmatrix}\beta_1 \\ \beta_2\end{bmatrix} + \begin{bmatrix}e_{3} \\ e_{4}\\ \vdots \\ e_{m} \end{bmatrix}$$
-
-The BLUE estimator of $\beta=[\beta_1,\beta_2]^T$ is
-
-$$\hat{\beta}=(\mathrm{A}^T\mathrm{A})^{-1}\mathrm{A}^TS$$
-
-
-## Worked example - Single Differencing
-
-On this worked example, we will show that [single differencing](SD) induces an MA(1) process. The original time series is given as:
-
-$$Y=\begin{bmatrix}Y_1\\ Y_2\\ \vdots \\ Y_m\end{bmatrix}, \hspace{10px} \Sigma_{Y}=\sigma^2 I_m$$
-
-We apply single differencing which in this case results in a purely random process:
-
-$$\begin{cases}S_1 = \Delta Y_1 = Y_1\\ S_2=\Delta Y_2 = Y_2 - Y_1\\ S_3=\Delta Y_3 = Y_3-Y_2\\ \quad\vdots \\ S_m= \Delta Y_m = Y_m - Y_{m-1}\end{cases}$$
-
-In matrix notation, this can be written as:
-
-$$\begin{bmatrix} S_1\\  S_2\\ \vdots \\  S_m\end{bmatrix} = \underbrace{\begin{bmatrix}
-    1 & 0 &   & \dots & 0\\
-    -1 & 1 & 0 &   &  \\
-    0 & -1 & 1 & \ddots & \\
-    \vdots & \ddots &\ddots & \ddots & 0 \\
-    0 & \dots & 0 & -1 & 1
-\end{bmatrix}}_{\mathrm{T}}\begin{bmatrix}Y_1\\ Y_2\\ \vdots \\ Y_m\end{bmatrix} \Longleftrightarrow S = \mathrm{T}Y$$
-
-We apply the [variance propagation law](01_LinearProp):
-
-$$\Sigma_{ S}=\mathrm{T}\Sigma_{Y}\mathrm{T}^T = \mathrm{T}\sigma^2I_m\mathrm{T}^T=\sigma^2\mathrm{TT}^T$$
-
-such that we obtain:
-
-$$\Sigma_{S} = \sigma^2\mathrm{TT}^T = 2\sigma^2\begin{bmatrix}1&-0.5&0&\dots&0\\ -0.5&1&-0.5& &\\ 0&-0.5&1&\ddots&0\\ \vdots& &\ddots&\ddots&-0.5\\ 0&\dots&0&-0.5&1\end{bmatrix}$$
-
-We can see that the structure indeed corresponds with the covariance matrix of an AR(1) process, from which we see that $\rho_1=-0.5$. Now we can find the value of $\theta$: 
-
-$$\begin{cases}\rho_1=-0.5=\frac{\theta}{1+\theta^2}\\  S_t = \theta e_{t-1}+e_t\end{cases}\implies \theta=-1 \implies S_t = e_t-e_{t-1}$$
-
-:::{card} Exercise
-
-For the stationary AR(2) process, calculate the ACF at lag 1. In other words, calculate $\rho_1$.
-
-```{admonition} Solution
-:class: tip, dropdown
-
-For the AR($p$) process we know that $\mathbb{E}(S_t)=0$, and $Var(S_t)=\sigma^2$ ($\forall t$), and
-
-$$S_t = \beta_1S_{t-1}+\beta_2S_{t-2}+e_t=
-\begin{bmatrix}\beta_1 & \beta_2 & 1\end{bmatrix}\begin{bmatrix}S_{t-1} \\ S_{t-2} \\ e_t\end{bmatrix}$$
-
-To compute the autocovariance function at lag 1, $c_1$, we need to compute the covariance between $S_{t-1}$ and $S_t$, which is given as
-
-$$
-\begin{align*}
-c_1 &= \mathbb{E}(S_{t-1}S_t)
-= \mathbb{E}\left(S_{t-1}
-(\beta_1 S_{t-1} + \beta_2 S_{t-2} + e_t)
-\right)
-\\
-&= \beta_1 \mathbb{E}(S_{t-1}^2)
-+ \beta_2 \mathbb{E}(S_{t-2}S_{t-1})
-+ \mathbb{E}(S_{t-1}e_t)\\
-&= \beta_1 \sigma^2
-+ \beta_2 c_1
-\end{align*}$$
-
-
-which gives
-
-$$
-\beta_1 \sigma^2 = c_1(1-\beta_2)
-$$
-
-or, because $\rho_1=c_1/\sigma^2$:
-
-$$
-\rho_1=\frac{\beta_1}{1-\beta_2}
-$$
-
-```
-:::
-
-## Brief Summary
-
-The random processes (noise processes) considered here are:
-
-* ARMA($p,q$) process
-
-$$
-S_t = \sum_{i=1}^p \beta_iS_{t-i}+e_t+\sum_{i=1}^q\theta_ie_{t-1}
-$$
-
-* AR($p$) process
-
-$$
-S_t = \sum_{i=1}^p \beta_iS_{t-i}+e_t
-$$
-
-* MA($q$) process
-
-$$
-S_t = e_t+\sum_{i=1}^q\theta_ie_{t-1}
-$$
-
-The parameters of these stochastic processes can be estimated using the least-squares method. This allows then to predict the stochastic process, needed for [forecasting](forecast).
\ No newline at end of file
diff --git a/book/time_series/components.md b/book/time_series/components.md
index 3f14a52fe826ef8881c216668d685fbb71e6cf96..530981306ba40d6c818be6436a5710167ad6e70b 100644
--- a/book/time_series/components.md
+++ b/book/time_series/components.md
@@ -1,7 +1,9 @@
 (components)=
 # Components of time series
 
-A time series is a sequence of data points indexed in time in order to study a phenomenon. It is thus the data collected at different points in time. The data are usually collected at fixed time intervals rather than just recording them intermittently or randomly. The fixed interval, in the time domain, is defined as 'sampling interval' and, in the frequency domain, is defined as 'sampling rate' or 'sampling frequency', expressed for example in Hz.
+A time series is a discrete time sequence of data points indexed in time which can be used to study a phenomenon. It is a record of the data collected at different points in time, it consists of discrete time samples of typically a continuous-time phenomenon in reality.  The data are usually collected at fixed time intervals rather than just recording them intermittently or randomly. The fixed interval $\Delta t$, in the time domain, is defined as 'sampling interval' and, in the frequency domain, is defined as 'sampling rate' or 'sampling frequency', expressed for example in Hz.
+
+$$ \Delta t = \frac{1}{f_s} $$
 
 A time series is denoted as 
 
@@ -23,7 +25,7 @@ Example of time series with equally spaced time interval $\Delta t$
 
 A time series can be decomposed as follows:
 
-$$Y(t) = tr(t) + s(t) + o(t) + b(t) + N(t)$$
+$$Y(t) = tr(t) + s(t) + o(t) + b(t) + \epsilon(t)$$
 
 where we distinguish the following components:
 
@@ -31,11 +33,11 @@ where we distinguish the following components:
 2. $s(t)$ = seasonality, shows the regular seasonal variations
 3. $o(t)$ = offset, is a discontinuity (or jump) in the data
 4. $b(t)$ = irregularities and outliers (also referred to as biases), due to unexpected reasons. Irregularities will not be considered in this book.
-5. $N(t)$ = noise, can be white or colored noise.
+5. $\epsilon(t)$ = noise process, can be white or colored noise.
 
 ## Trend
 
-The trend is the general pattern of the time series and shows its long-term changes. It is observed when there is an increasing or decreasing slope in the time series.
+The trend is the general pattern of the time series and shows its long-term changes. The trend can be linear, however higher order polynomials are also possible.
 
 ```{figure} ./figs/trend.png
 :name: trend
@@ -47,25 +49,8 @@ Monthly time series of global mean sea level measurements using Satellite Altime
 
 {numref}`trend` shows a positive trend (red line) of around $3.5$ mm/year, which in this case indicates sea level rise. This however needs to be further investigated and tested statistically (see {ref}`hypothesis_testing` and also {ref}`modelling_tsa`).
 
-Trend analysis expresses the changes of the variable of interest with respect to time $t$.
-We address here two types of trend analysis.:
-
-1. Linear trend analysis. The time-dependent variable $Y(t)$ changes at a (constant) linear rate over time: $Y_t = y_0 + r t + \epsilon_t$
-2. Log-linear trend analysis. The time-dependent variable changes at a (constant) exponential rate over time: $ln(Y_t) = y_0 + r t + \epsilon_t$
-
-:::{card} Example - linear trend
-
-Assume $\hat y_0 = 5$ and $\hat r = 2$.
-
-Therefore, $Y_t = \hat y_0 + \hat r  t = 5 + 2  t$. At $t=10$, $Y_{10} = 5 + 2 \times 10 = 25$.
-:::
-
-:::{card} Example - log-linear trend
+Trend analysis expresses the changes of the variable of interest with respect to time $t$. Different types of trend are possible and for now we will mainly focus on linear trend, i.e. the time-dependent variable $Y(t)$ changes at a (constant) linear rate over time: $Y_t = y_0 + r t + \epsilon_t$. Other trends are however also possible, for example, quadratic, which includes $c t^2$, or log linear $\log(Y_t) = y_0 + r t + \epsilon_t$.
 
-Assume again $\hat y_0 = 5$ and $\hat r = 2$.
-
-$ln(Y_t) = \hat y_0 + \hat r  t = 5 + 2  t$. At $t=10$, $ln(Y_{10}) = 5 + 2 \times 10 \Rightarrow Y_{10} = 72004899337.4$
-:::
 
 ## Seasonality
 
@@ -73,73 +58,85 @@ Seasonal variations explain regular fluctuations in a certain period of time (e.
 
 From {numref}`trend` it is also possible to see the seasonal variations: in fact sea levels are higher in summer and lower in winter. The annual warming/cooling cycle is the main contributor to these seasonal variations.
 
-Regular seasonal variations in a time series might be handled by using a sinusoidal model with one or more sinusoids whose frequency may be known or unknown depending on the context. A harmonic model for seasonal variation can be of the following two equivalent forms (using that $\sin(u+v)= \sin u \cos v + \cos u \sin v$):
+Regular seasonal variations in a time series might be handled by using a sinusoidal model with one or more sinusoids with frequency that may be known or unknown depending on the context. In fig {numref}`trend`, cyclical behavior with a period of 1 year can be observed. A harmonic model for seasonal variation can be of the following two equivalent forms (using that $\cos(u+v)= \cos u \cos v - \sin u \sin v$):
 
 $$ 
 \begin{align*}
-Y(t) &= \sum_{k=1} ^p A_k  \sin(k \omega_0  t + \theta_k)  + \epsilon_t\\
+Y(t) &= \sum_{k=1} ^p A_k  \cos(k \omega_0  t + \theta_k)  + \epsilon_t\\
 &= \sum_{k=1} ^p \left(a_k  \cos(k \omega_0  t) + b_k  \sin(k \omega_0 t) \right)+ \epsilon_t
 \end{align*}
 $$
 
-with the coefficients $a_k =A_k\sin\theta_k$ and $b_k=A_k\cos\theta_k$, and where $\omega_0$ is the base (fundamental) frequency of the seasonal variation and is fixed or is determined by Spectral Analysis methods such as {ref}`dft` or FFT. To be more specific, we can use the {ref}`psd` and {ref}`LS-HE` to determine the unknown frequencies. 
+With the coefficients $a_k = A_k\cos\theta_k$ and $b_k=-A_k\sin\theta_k$, and where $\omega_0$ is the base (fundamental) frequency of the seasonal variation and is fixed or is determined by Spectral Analysis. To be more specific, we can use the {ref}`psd` to determine the unknown frequencies. 
 
-The coefficients $a_k $ and $b_k$ can be determined using the least-squares method. From this the original sinusoids can be obtained using:
+Once $\omega_ 0$ is set, the coefficients $a_k $ and $b_k$ can be determined using the least-squares method, since the equation is linear in $a_k$ and $b_k$. From this the original sinusoids can be obtained using:
 
-$$ A_k = \sqrt{a_k^2 + b_k^2}, \hspace{1cm} \theta_k = \arctan(\frac{a_k}{b_k}), \hspace{1cm} k = 1, \ldots{}, p $$
+$$ A_k = \sqrt{a_k^2 + b_k^2}, \hspace{1cm} \theta_k = \arctan(-\frac{b_k}{a_k}), \hspace{1cm} k = 1, \ldots{}, p $$
+
+```{note}
+This transformation is necessary to make the seasonal component phase-independent. Using regular estimation methods, we cannot linearly estimate the phase of the sinusoidal function. However by transforming the sinusoidal function into a linear combination of sine and cosine functions, we can estimate the phase of the seasonal component.
+```
 
 :::{card} Worked example - seasonality signal
 
 Show that the time series 
 
-$$Y(t)=A sin(\omega_0 t + \theta)$$ 
+$$Y(t)=A \cos(\omega_0 t + \theta)$$ 
 
 with given $\omega_0$, can be rewritten as
 
-$$Y(t)=a cos(\omega_0 t) + b sin(\omega_0 t)$$
+$$Y(t)=a \cos(\omega_0 t) + b \sin(\omega_0 t)$$
 
 and derive the formulation of $A$ and $\theta$.
 
-Hint: you might need to know sine properties $sin(u+v)=sin(u)cos(v)+cos(u)sin(v)$
+Hint: you might need to know trigonometric identity $\cos(u+v)=\cos(u)\cos(v)-\sin(u)\sin(v)$
 
 ````{admonition} Solution
 :class: tip, dropdown
 
-[This video](https://youtu.be/8kqQiI4ni68) includes the solution to this exercise. 
-````
+Using the trigonometric identity to rewrite:
 
-:::
+$ Y(t)=A \cos(\omega_0 t + \theta) = A (\cos(\omega_0 t)\cos(\theta)-\sin(\omega_0 t)\sin(\theta)) $
 
-(season)=
-:::{card} Example - seasonal variations
+Retrieving the functions for a and b
 
-```{figure} ./figs/sine_wave_1.jpg
-:name: trendab
-:width: 600px
-:align: center
+$ a = A \cos(\theta) \hspace{1cm} b = -A \sin(\theta)$
 
-Seasonal variations components: blue line is the time series $Y(t)$; red and green lines represent the contributions $a  \cos(0.5\pi t)$ and  $b   \sin(0.5\pi t)$, respectively.
-```
+Squaring both functions in order to get rid of the sin and cos
+
+$ a^2 = A^2 \cos^2(\theta) \hspace{1cm} b^2 = A^2 \sin^2(\theta) $
+
+Adding both functions together
+
+$ a^2 + b^2 = A^2 (\cos^2(\theta) + \sin^2(\theta)) $
+
+Using this property to simplify:
 
-The seasonal variation is given as $y = A \sin(\omega_0 t + \theta)$.
+$ \cos^2(\theta) + \sin^2(\theta) = 1 $
 
-Assume amplitude $A=2$, base frequency $\omega_0=0.5\pi$ and initial phase $\theta = -0.8 \pi$ (rad), see top panel of {numref}`trendab`.
+$ a^2 + b^2 = A^2 $
 
-$y(t) = 2 \sin(0.5 \pi t - 0.8\pi)$
+Take square root to find A
 
-The time-delay of the phase is $0.5 t - 0.8 = 0 \Rightarrow t = 1.6 \equiv \theta_t$.
+$ \sqrt{a^2 + b^2} = A $ 
 
-Alternatively we can write 
+For $\theta$ we rewrite the second function
 
-$y(t) = a  \cos(0.5\pi t) + b   \sin(0.5\pi t)$
+$ a = A \cos(\theta) \hspace{1cm} -b = A \sin(\theta)$
 
-where $a = A  \sin(\theta)=-1.1756$ and $b=A  \cos(\theta)=-1.6180$.
+$ \frac{-b}{a} = \frac{\sin(\theta)}{\cos(\theta)} = \tan(\theta) $
+
+$ \theta = \arctan(\frac{-b}{a}) $
+
+
+[This video](https://youtu.be/8kqQiI4ni68) includes the solution to this exercise. 
+````
 
 :::
 
 ## Offset (jump)
 
-Offsets are sudden changes in time series. There are different underlying reasons why we encounter offsets in time series. 
+Offsets are sudden changes or shifts in time series. There are different underlying reasons why we encounter offsets in time series. 
 
 ```{figure} ./figs/offset.png
 :name: offset
@@ -149,9 +146,9 @@ Offsets are sudden changes in time series. There are different underlying reason
 Example of time series with two offsets. 
 ```
 
-As a deterministic sudden change, offsets can be handled by a step function such as a Heaviside step function whose epoch (time instant) can be known or unknown (to be detected) depending on the time series.
+As a deterministic sudden change, offsets can be handled by a step function such as a heaviside step function with an epoch (time instant) that can be known or unknown (to be detected) depending on the time series.
 
-In this case the time series is written as 
+In this case the time series is written as: 
 
 $$ Y(t) = \sum_{k=1}^q o_k u_k(t)+\epsilon_t$$
 
@@ -164,21 +161,23 @@ $$u_k(t) = \left\{
 \end{array} 
 \right.  $$
 
+Once the time instant ($t_k$) of the offset is known, the amplitude can be estimated using least-squares.
+
 ## Noise 
 
 Noise simply refers to random fluctuations in the time series about its typical pattern. In general we can talk about white and colored noise in time series analysis. The following characteristics are associated with noise:
 
-- Noise is not necessarily synonymous to error, but part of noise is the random error.
+- Noise is not synonymous with error, although random variation, including measurement errors, contributes to noise. Essentially, noise represents the unpredictable fluctuations in data, while errors encompass any inaccuracies that may arise from a range of factors, including both random variations and systematic issues.
 - It is required to filter out unwanted random variations, and detect meaningful information (i.e., a signal) from noise processes.
 - Transforming data from the time domain to the frequency domain allows to filter out the frequencies that pollute the data.
-- White noise can be decomposed into its constituent components (frequencies) like white light.  In principle, white noise contains all wavelengths/colors, each contributing equally to the fluctuations observed in the data.
+- White noise can be decomposed into its constituent components (frequencies).  In principle, white noise contains all wavelengths/colors (like white light), each contributing equally to the fluctuations observed in the data.
 - Colored noise can seriously affect the analysis of time series, and their parameters of interest. Short-term colored noise has also predictive property (used for forecasting).
 
-A purely random process (or white noise process) yields a sequence of uncorrelated zero-mean random variables. This zero-mean random process is of the form
+A purely stationary random process (or white noise process) yields a sequence of uncorrelated zero-mean random variables. This zero-mean random process is of the form
 
 $$ Y(t)=Y_t=\epsilon_t $$
 
-where $\epsilon_t$ is the independent identically distributed (i.i.d.) error at epoch $t$. Therefore, the observation/noise at time $t$ is not dependent on the previous observations.
+where $\epsilon_t$ is the independent identically distributed (i.i.d.) error at epoch $t$. Therefore, the observation/noise at time $t$ is not dependent on any of the previous observations $Y_t$.
 
 ### Stochastic model
 
@@ -194,7 +193,7 @@ $$
 \mathbb{D}(Y) =  \Sigma_{Y} = \sigma^2 \left[\begin{array}{ccc} 1 & 0 & \ldots{} & 0 \\ 0 & 1 & \ldots{} & 0 \\ \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & \ldots{} & 1 \end{array}\right]
 $$
 
-The noise can be represented with a Gaussian distribution with mean $\mu=0$ and variance $\sigma^2$, that is $\epsilon(t) \sim \textbf{N}(0, \sigma^2)$.
+The noise can be represented with, for example, a Gaussian distribution with mean $\mu=0$ and variance $\sigma^2$, that is $\epsilon(t) \sim \textbf{N}(0, \sigma^2)$.
 
 :::{card} Example - time series consisting of a trend, annual signal (seasonality), an offset and pure random noise (white noise)
 
@@ -208,5 +207,6 @@ where
 - $a$ and $b$ are the coefficients of the signal, (e.g. annual signal)
 - $\omega_0$ is the frequency (e.g. 1 cycle/year)
 - $o$ is the offset starting at time $t_k$
-- $\epsilon(t)$ is the i.i.d. random noise, i.e. $\epsilon(t) \sim \textbf{N}(0, \sigma^2)$.
+- $u_k(t)$ is the Heaviside step function
+- $\epsilon(t)$ is the i.i.d. random Gaussian noise, i.e. $\epsilon(t) \sim \textbf{N}(0, \sigma^2)$.
 :::
\ No newline at end of file
diff --git a/book/time_series/exercise1.ipynb b/book/time_series/exercise1.ipynb
index b0b19b93633837fd3b776cc1849d100aaa93488c..e8c94446388bd96051ecc5fc380fc662dad2a55d 100644
--- a/book/time_series/exercise1.ipynb
+++ b/book/time_series/exercise1.ipynb
@@ -24,16 +24,17 @@
    "source": [
     "**Introduction:** \n",
     "\n",
-    "The four components of time series are the trend, seasonality, offset, and noise (white/colored). We use simulated data to show these components here. The observation equation of time series should have the following mathematical representation:\n",
+    "The four components of time series, we will consider here, are the trend, seasonality, offset, and noise (white/colored). We use simulated data to show these components here. The observation equation of time series should have the following mathematical representation:\n",
     "\n",
-    "$$Y(t) = y_0 + r t + a \\cos(\\omega_ot) + b\\sin(\\omega_ot) + o {u_k(t)} + \\epsilon(t)= y_0 + r t + A \\sin(\\omega_o t+\\phi_0) + o {u_k(t)} + \\epsilon(t)$$\n",
+    "$$Y(t) = y_0 + r t + a \\cos(\\omega_0t) + b\\sin(\\omega_0t) + o {u_k(t)} + \\epsilon(t)= y_0 + r t + A \\cos(\\omega_0 t+\\phi_0) + o {u_k(t)} + \\epsilon(t)$$\n",
     "\n",
     "where\n",
     "- $y_0 $: intercept (e.g. in mm)\n",
     "- $r$: is the rate (e.g. in mm/day)\n",
     "- $a$ and $b$ are the coefficients of the periodic signal \n",
-    "- $\\omega$ is the frequency of signal (e.g. cycle/ day)\n",
+    "- $\\omega_0$ is the frequency of signal (e.g. cycle/ day)\n",
     "- $o$ is the size of the offset at time instant $t_k$\n",
+    "- $u_k(t)$ is the unit step function which is 1 if $t_k \\leq t$ and 0 otherwise\n",
     "- $\\epsilon(t)$ is the random noise with a given variance which follows a Normal distribution: $ \\epsilon(t) \\sim \\textbf{N}(0, \\sigma^2)$\n",
     "\n",
     "Here, we are assuming only a single seasonality and offset component. However, in many practical scenarios, there could be multiple components related to these.\n",
@@ -59,17 +60,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, '$Y$(t) = 1 + 0.02 t $')"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
@@ -92,14 +83,14 @@
     "plt.plot(time, y1, color='red')\n",
     "plt.ylabel('Y(t)')\n",
     "plt.xlabel('Time (day)')\n",
-    "plt.title('$Y$(t) = 1 + 0.02 t $')"
+    "plt.title('$Y(t) = 1 + 0.02 t $');"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We then introduce seasonality to the data with a sine signal $s(t)=A sin(\\omega t + \\phi_0)$ with $\\omega=2\\pi f$, frequency $f=0.01$ cycle/day (i.e., 1 cycle per 100 days), amplitude $A=1$ mm and initial phase $\\phi_0=0.2\\pi$ rad."
+    "We then introduce seasonality to the data with a cosine signal $s(t)=A \\cos(\\omega_0 t + \\phi_0)$ with $\\omega_0=2\\pi f$, frequency $f=0.01$ cycle/day (i.e., 1 cycle per 100 days), amplitude $A=1$ mm and initial phase $\\phi_0=0.2\\pi$ rad."
    ]
   },
   {
@@ -109,17 +100,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, '$Y(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€)$')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
@@ -129,17 +110,17 @@
     }
    ],
    "source": [
-    "omega = 2 * np.pi/100 \n",
+    "omega_0 = 2 * np.pi/100 \n",
     "A = 1 \n",
     "phi_0 = 0.2*np.pi \n",
-    "y2 = y1 + A*np.sin(omega * time + phi_0) \n",
+    "y2 = y1 + A*np.cos(omega_0 * time + phi_0) \n",
     "\n",
     "plt.figure(figsize=(8,4))\n",
     "plt.grid()\n",
     "plt.plot(time, y2, color='blue')\n",
     "plt.ylabel('Y(t)')\n",
     "plt.xlabel('Time (day)')\n",
-    "plt.title('$Y(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€)$')"
+    "plt.title('$Y(t) = 1 + 0.02 t + cos(0.02Ï€t + 0.2Ï€)$');"
    ]
   },
   {
@@ -156,17 +137,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, '$Y(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€) + 5 u_{300}(t)$')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
@@ -186,7 +157,7 @@
     "plt.plot(time, y3, color='g')\n",
     "plt.ylabel('Y(t)')\n",
     "plt.xlabel('Time')\n",
-    "plt.title('$Y(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€) + 5 u_{300}(t)$')"
+    "plt.title('$Y(t) = 1 + 0.02 t + cos(0.02Ï€t + 0.2Ï€) + 5 u_{300}(t)$');"
    ]
   },
   {
@@ -203,17 +174,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, '$Y(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€) + 5 u_{300}(t) + N(0,0.5^2)$')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
@@ -233,13 +194,13 @@
     "plt.plot(time, y4, color='red')\n",
     "plt.ylabel('Y(t)')\n",
     "plt.xlabel('Time')\n",
-    "plt.title('$Y(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€) + 5 u_{300}(t) + N(0,0.5^2)$')"
+    "plt.title('$Y(t) = 1 + 0.02 t + cos(0.02Ï€t + 0.2Ï€) + 5 u_{300}(t) + N(0,0.5^2)$');"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "mude2",
+   "display_name": "TAMude",
    "language": "python",
    "name": "python3"
   },
@@ -253,7 +214,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,
diff --git a/book/time_series/exercise2.ipynb b/book/time_series/exercise2.ipynb
deleted file mode 100644
index 8b3a7eb62b83fcb11aa1f681cbc9394b7bfeafa9..0000000000000000000000000000000000000000
--- a/book/time_series/exercise2.ipynb
+++ /dev/null
@@ -1,251 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Stationary time series "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from statsmodels.tsa.stattools import adfuller \n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Introduction:** \n",
-    "\n",
-    "[In the previous exercise](https://mude.citg.tudelft.nl/book/time_series/exercise1.html#) we created and plotted the $Y_4$ time series, now we check its stationarity. Remember that we need to ensure *stationarity* of the time series data-set for *forecasting and predictive models*. \n",
-    "In this excercise, you can test the stationarity of the time series using transformation and visual inspection and the Augmented Dickey-Fuller (ADF) test (The ADF test is optional). \n",
-    "\n",
-    "**Background knowledge:** \n",
-    "\n",
-    "The ADF test can be performed by using two hypotheses (Null Hypothesis and Alternative Hypothesis):\n",
-    "\n",
-    "1. Null Hypothesis $H_o$: we assume that the time series is not stationary. \n",
-    "2. Althernative Hypothesis $H_a$: we assume that the time series is stationary. \n",
-    "\n",
-    "If the test statistic is smaller than the critical value, the null hypothesis is rejected and therefore the time series is stationary. In this case the the p-value becomes very small. In python, there is a package: **statsmodels** which has the function of **adfuller method**. We use the <code>adfuller()</code> function to test the stationarity of the data-set. Regarding the interpretation of the adfuller function, the first output is the test-statistic, the second one is the p-value, etc.\n",
-    "\n",
-    "**Excercise:** \n",
-    "\n",
-    "We take the time series and the noise from the Excercise 1 $Y_2$, $Y_4$ and $\\epsilon_t$. We also use the single differencing method to make the time series stationary and plot the results. Later we will also use the least squares method (best linear unbiased estimation - BLUE) to de-trend the data. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Note:**\n",
-    "\n",
-    "You don't need to focus on the next cell, it contains the code included in the [Time series components exercise](https://mude.citg.tudelft.nl/book/time_series/exercise1.html#) for creating the time series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.random.seed(0)  # For reproducibility\n",
-    "\n",
-    "# create observations\n",
-    "time = np.arange(501) \n",
-    "m = len(time)\n",
-    "y_0 = 1 \n",
-    "r = 0.02 \n",
-    "y1 = y_0 + r*time \n",
-    "\n",
-    "# introduce a seasonality\n",
-    "omega = 2 * np.pi/100 \n",
-    "A = 1 \n",
-    "phi_0 = 0.2*np.pi\n",
-    "y2 = y1 + A*np.sin(omega * time + phi_0) \n",
-    "\n",
-    "# introduce offset\n",
-    "t_k = 300 \n",
-    "O_k = 5 \n",
-    "y3 = y2.copy() \n",
-    "y3[t_k:] = y3[t_k:] + O_k\n",
-    "\n",
-    "# introduce random error\n",
-    "mean = 0 \n",
-    "sigma = 0.5 \n",
-    "et = np.random.normal(loc = mean, scale = sigma, size = m) \n",
-    "y4 = y3 + et "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We start applying single differencing to check whether the time series becomes stationary. We do it first for <code>y2</code>, which just contains the observations and the seasonality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Single Differencing')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "diff_y2 = np.diff(y2)\n",
-    "diff_y2 = np.insert(diff_y2, 0, 0)\n",
-    "\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, diff_y2)\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time')\n",
-    "plt.title('Single Differencing')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<code>y2</code> clearly looks non-stationary. Can you explain why?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We repeat the above procedure for <code>y4</code>, which also includes offset and noise."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Single Differencing')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "diff_y4 = np.diff(y4)\n",
-    "diff_y4 = np.insert(diff_y4, 0, 0)\n",
-    "\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, diff_y4)\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time')\n",
-    "plt.title('Single Differencing')\n",
-    "# it looks stationary, but we show it using ADF test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<code>y4</code> now seems to be stationary, but to prove it we need to do the ADF test, which is optional material."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "ADF test is used to show that the single has produced a stationary dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test statistics:-15.24, pvalue:0.0000, Critical_value(1%):-3.44\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_diff_y4 = adfuller(diff_y4)\n",
-    "test_statistic = test_diff_y4[0]\n",
-    "p_value = test_diff_y4[1]\n",
-    "critical_value = test_diff_y4[4]\n",
-    "print(f'Test statistics:{test_statistic:.2f}, pvalue:{p_value:.4f}, Critical_value(1%):{critical_value[\"1%\"]:.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since the test statistic is smaller than the critical value and the p-value is small, the Null hypothesis $H_0$ is rejected. This means that the time series is stationary!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "mude2",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/book/time_series/exercise3.ipynb b/book/time_series/exercise3.ipynb
deleted file mode 100644
index 9fa20c445fd0e599b669329e16c2ae59d8b78baf..0000000000000000000000000000000000000000
--- a/book/time_series/exercise3.ipynb
+++ /dev/null
@@ -1,235 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Time series modelling "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from statsmodels.graphics.tsaplots import plot_acf\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Introduction:** \n",
-    "\n",
-    "In this exercise, you will focus on the Best Linear Unbiased Estimation (BLUE). With BLUE, if the components of the time series are known, you can use the linear model of observations to estimate these components. \n",
-    "\n",
-    "**Exercise:** \n",
-    "\n",
-    "In this excercise, you calculate the BLUE estimates. First, create your matrix $A$ and $\\Sigma_{Y}$ which need to have dimensions of 501x5 (501: rows and 5 columns) and 501x501 respectively. Can you explain what these 5 parameters are? For $\\Sigma_{Y}$, you can use the np.eye function from numpy. Having defined these two matrices, we can obtain the BLUE estimats of \n",
-    "\n",
-    "$$\n",
-    "\\hat{X}=(A^T \\Sigma_{Y}^{-1}A)^{-1}A^T \\Sigma_{Y}^{-1}Y,\\, \\, \\hat{Y}=...,\\, \\, \\hat{\\epsilon}=...\n",
-    "$$ \n",
-    "\n",
-    "along with their covariance matrices $\\Sigma_{\\hat{X}}=(A^T \\Sigma_{Y}^{-1}A)^{-1}$, $\\Sigma_{\\hat{Y}}=...$ and $\\Sigma_{\\hat{\\epsilon}}=...$. \n",
-    "\n",
-    "After you have estimated the $\\hat{X}$ (having 5 elements), we you can compare each element of the $\\hat{x}$ with the corresponding values from the original time series you simulated ($y_0$, $r$, $A_m$, $\\phi$, $o_k$). The precision of the parameters can also be obtained from $\\Sigma_{\\hat{X}}$. You may also want to follow hypothesis tests to test the statistical significance of the estimated parameters. \n",
-    "\n",
-    "Please note that $A_m$ has been previously defined as $A$, but for avoiding confusion with the $A$ matrix has been renamed."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Note:**\n",
-    "\n",
-    "You don't need to focus on the next cell, it contains the code included in the [Time series components exercise](https://mude.citg.tudelft.nl/book/time_series/exercise1.html#) for creating the time series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.random.seed(0)  # For reproducibility\n",
-    "\n",
-    "# create observations\n",
-    "time = np.arange(501) \n",
-    "m = len(time)\n",
-    "y_0 = 1 \n",
-    "r = 0.02 \n",
-    "y1 = y_0 + r*time \n",
-    "\n",
-    "# introduce a seasonality\n",
-    "omega = 2 * np.pi/100 \n",
-    "Am = 1 \n",
-    "phi_0 = 0.2*np.pi\n",
-    "y2 = y1 + Am*np.sin(omega * time + phi_0) \n",
-    "\n",
-    "# introduce offset\n",
-    "t_k = 300 \n",
-    "O_k = 5 \n",
-    "y3 = y2.copy() \n",
-    "y3[t_k:] = y3[t_k:] + O_k\n",
-    "\n",
-    "# introduce random error\n",
-    "mean = 0 \n",
-    "sigma = 0.5 \n",
-    "et = np.random.normal(loc = mean, scale = sigma, size = m) \n",
-    "y4 = y3 + et "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We first create the $A$ matrix which is based on linear regression and seasonality. We then include the offset in a new column and consequently create the covariance matrix $\\Sigma_Y$. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = np.stack((np.ones(m), time, np.cos(omega*time), np.sin(omega*time)), axis=1)\n",
-    "u = np.zeros(m)\n",
-    "u[t_k:] = 1\n",
-    "A = np.column_stack((A,u))\n",
-    "Sigma_Y = (sigma**2) * np.eye(m) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now implement BLUE, meaning that we compute $\\hat X$, $\\hat Y$, \\hat \\psilon$ and the covariance matrix $\\Sigma_{\\hat{X}}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Xhat = np.linalg.inv(A.T @ np.linalg.inv(Sigma_Y) @ A) @ A.T @ np.linalg.inv(Sigma_Y) @ y4\n",
-    "Yhat = A @ Xhat \n",
-    "ehat = y4 - Yhat \n",
-    "Sigma_xhat = np.linalg.inv(A.T @ np.linalg.inv(Sigma_Y) @ A)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We then compare the observed (true) values of $y_0, r, A_m, \\phi_0$ and $o_k$ with the predicted ones."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "y0: True value is: 1 ,  Estimated value is: 1.0108670424240747\n",
-      "r: True value is: 0.02 ,  Estimated value is: 0.020019382911012365\n",
-      "Am: True value is: 1 ,  Estimated value is: 1.0971057283981764\n",
-      "phi0: True value is: 0.6283185307179586 ,  Estimated value is: 0.6899387295464887\n",
-      "Ok: True value is: 5 ,  Estimated value is: 4.929702224035138\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Comparisons of xhat with the initial (true) values x:\n",
-    "y_0_hat = Xhat[0] # compare with y_0\n",
-    "print('y0: True value is:', y_0,',  Estimated value is:', y_0_hat)\n",
-    "r_hat = Xhat[1]   # compare with r\n",
-    "print('r: True value is:', r,',  Estimated value is:', r_hat)\n",
-    "\n",
-    "Am_hat = np.sqrt(Xhat[2]**2 + Xhat[3]**2) # compare with Am\n",
-    "print('Am: True value is:', Am,',  Estimated value is:', Am_hat)\n",
-    "\n",
-    "phi_0_hat = np.arctan(Xhat[2]/Xhat[3]) # compare with phi_0\n",
-    "print('phi0: True value is:', phi_0,',  Estimated value is:', phi_0_hat)\n",
-    "\n",
-    "O_k_hat = Xhat[4]  # compare with O_k\n",
-    "print('Ok: True value is:', O_k,',  Estimated value is:', O_k_hat)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now plot the observed and estimated time series. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1fd586d5a50>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 5))\n",
-    "plt.grid()\n",
-    "plt.plot(time, y4, label='Original $Y$ (with noise)', color='pink')\n",
-    "plt.plot(time, Yhat, label='Estimated $Y$: yhat', color='r')\n",
-    "plt.plot(time, y3, label='True $Y$ (without noise)', linestyle='--', color='b')\n",
-    "plt.title('Time series modelling')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('time')\n",
-    "plt.legend();"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "mude2",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/book/time_series/exercise4.ipynb b/book/time_series/exercise4.ipynb
deleted file mode 100644
index 4362fa96e4eb0237091f242ff961965acb54ce8a..0000000000000000000000000000000000000000
--- a/book/time_series/exercise4.ipynb
+++ /dev/null
@@ -1,196 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Autocovariance function (ACF) and PSD "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from statsmodels.graphics.tsaplots import plot_acf\n",
-    "from scipy import signal\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Introduction:** \n",
-    "\n",
-    "In this exercise, you will focus on normalized auto-covariance function (ACF) and the power spectral density (PSD), and the auto-regressive moving average (ARMA).\n",
-    "\n",
-    "**Background knowledge:** \n",
-    "\n",
-    "The package <code>statsmodels.graphics.tsaplots</code> contains functions for computing ACF and PSD. <code>plot_acf</code> is one of these functions, which create automatically a plot. Regarding the PSD, there is a function from the package of <code>scipy.signal</code> and you need to use the <code>signal.periodogram</code> to calculate the PSD. An alternative way to compute the PSD is based on the least-squares harmonic estimation (LS-HE), which is based on hypothesis testing.\n",
-    "\n",
-    "**Exercise:** \n",
-    "\n",
-    "We use the above functions to plot the ACF and PSD of white noise time series. Later we also compute them for the ARMA(p,q) process. We generate a white noise process, similar to that created in [Time series components exercise](https://mude.citg.tudelft.nl/book/time_series/exercise1.html#) ($m=501$). We will see that white noise does not show any temporal correlation. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We start defining the parameters of the white noise $\\epsilon \\sim \\textbf{N} (\\mu=0, \\sigma_{\\epsilon}^2=1)$. As previously done in the [Time series components exercise](https://mude.citg.tudelft.nl/book/time_series/exercise1.html#), the number of observation is $m=501$ and the time interval is $\\Delta t = 1$ s. The sampling rate is chosen equal to $f_s=1$ Hz."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Time (s)')"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mean1 = 0 \n",
-    "sigma1 = 1\n",
-    "m = 501\n",
-    "time = np.arange(m) \n",
-    "Fs = 1 \n",
-    "\n",
-    "e = np.random.normal(loc = mean1, scale = sigma1, size = m) \n",
-    "yt = e\n",
-    "\n",
-    "# plot the time series\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(time, yt, color='pink')\n",
-    "plt.title(r'White noise $\\epsilon$ time series')\n",
-    "plt.ylabel(r'$Y$(t)')\n",
-    "plt.xlabel('Time (s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now plot the normalized auto-covariance function (ACF) of the generated noise.\n",
-    "Look at the time series. Do you see temporal correlation?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ACF = plot_acf(yt, lags=None, alpha=0.05, title=r'ACF of white noise $\\epsilon$', color='pink')\n",
-    "plt.grid()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also plot the PSD of the white noise. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Frequency (Hz)')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot the white noise PSD\n",
-    "F, PSD = signal.periodogram(yt, fs=Fs, scaling='density', return_onesided=False)\n",
-    "\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(F, PSD, color='pink')\n",
-    "plt.title(r'PSD of white noise $\\epsilon$')\n",
-    "plt.ylabel('Power PSD')\n",
-    "plt.xlabel('Frequency (Hz)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The PSD values seem not to be frequency dependent: it looks flat indicating that all frequencies have identical contributions, even though there are some peaks. Think of white light, that has similar characteristics."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "mude2",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/book/time_series/exercise5.ipynb b/book/time_series/exercise5.ipynb
deleted file mode 100644
index 32ae8c5d0ff439afdc4bf1535da9b5390fc6a872..0000000000000000000000000000000000000000
--- a/book/time_series/exercise5.ipynb
+++ /dev/null
@@ -1,228 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ARMA: MA(1), ACF + PSD "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from statsmodels.graphics.tsaplots import plot_acf\n",
-    "from scipy import signal\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Introduction:** \n",
-    "\n",
-    "In this exercise, we will focus on special case of ARMA(p,q) process, namely ARMA(0,1)=MA(1) process. We then compare the ACF and PSD of the generated time series.\n",
-    "\n",
-    "**Exercise:** \n",
-    "\n",
-    "We generate a MA(1) time series. As you know from the lectures an MA(1) is of the form\n",
-    "\n",
-    "$$\n",
-    "Y_t = \\theta \\epsilon_{t-1}+ \\epsilon_{t}\n",
-    "$$\n",
-    "\n",
-    "We assume $\\theta=0.8$, and the time series is assumed to be stationary, so $\\mathbb{E}(Y_t)=0$ and $\\mathbb{D}(Y_t)=\\sigma^2$, with $\\sigma=1$. For generating the time series, we need an initialization of one sample generated randomly as $(0,\\sigma^2)$, and then use the above recursive formulae. The variance of $\\epsilon_t$ is obtained from\n",
-    "\n",
-    "$$\n",
-    "\\sigma_{\\epsilon}^2 = \\frac{\\sigma^2}{1+\\theta^2}\n",
-    "$$\n",
-    "\n",
-    "We can then apply the ACF and PSD to the generated MA(1) noise process."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We start defining the parameters of the white noise $\\epsilon \\sim \\textbf{N} (\\mu=0, \\sigma_{\\epsilon}^2=1)$. As previously done in the [Time series components exercise](https://mude.citg.tudelft.nl/book/time_series/exercise1.html#), the number of observation is $m=501$ and the time interval is $\\Delta t = 1$ s. The sampling rate is chosen equal to $f_s=1$ Hz.\n",
-    "\n",
-    "We also define $\\sigma_{\\epsilon}$ and create the arrays for $Y$ and $\\epsilon$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# simulate an ARMA(0,1)=MA(1) noise process (moving average of order 1)\n",
-    "mean2 = 0 \n",
-    "sigma2 = 1\n",
-    "theta = 0.8\n",
-    "m = 501\n",
-    "time = np.arange(m) \n",
-    "Fs = 1 \n",
-    "\n",
-    "sigma_e = np.sqrt(sigma2**2/(1+theta**2))\n",
-    "y = np.zeros(m) \n",
-    "e = np.zeros(m) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now initialize the first entry of <code>e</code> and <code>y</code> such that $Y(0)=\\epsilon(0)$ and then we can loop using the function $Y_t = \\theta \\epsilon_{t-1}+ \\epsilon_{t}$ defined above. Note that all entries of $\\epsilon$ are random numbers, but $\\epsilon (0)$ depends on $\\sigma^2$, while the others on $\\sigma_{\\epsilon}$.\n",
-    "\n",
-    "Then we plot the time series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'time')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "e[0] = np.random.randn() * sigma2\n",
-    "y[0] = e[0]\n",
-    "\n",
-    "for i in range(1, m):\n",
-    "    e[i] = np.random.randn() * sigma_e\n",
-    "    y[i] = theta * e[i-1] + e[i]\n",
-    "\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(time, y, color='pink')\n",
-    "plt.title('MA(1) time series')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('time')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now compute and plot the ACF of the MA(1) process."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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7cDqdcjqd7vtlZWWSJJfLJZfL1ZJNapTLVeXxfzP77mjqakcNW4c6moM6moM6mqMj17El2+zTgHP48GFVV1crJibGY3lMTIyKioqaXL+wsFDvvvuuXn/9dY/l/fr1U25urgYMGKCysjI988wzGjZsmHbt2qU+ffp49TN37lzNnj3ba/natWsVFhbWwq1qmLNaqivp+vXr5Qg0resOKy8vr62HYAnU0RzU0RzU0RwdsY7l5eXNbuvTgFPHZrN53DcMw2tZfXJzc9W5c2fdcMMNHsvT0tKUlpbmvj9s2DANGjRIzz33nJ599lmvfmbOnKmcnBz3/bKyMiUkJCgzM1ORkZEt3JqGlVdW6f5P1kuSRo4cqahwzgk6Wy6XS3l5eRo1apTsdntbD8dvUUdzUEdzUEdzdOQ61h2BaQ6fBpzo6GgFBgZ6zdYUFxd7zeqcyTAMLVq0SFlZWQoODm60bUBAgC655BLt2bOn3scdDoccDofXcrvdburOYTd+CG12e1CH2/F8wezfUUdFHc1BHc1BHc3REevYku316UnGwcHBSk1N9ZpGy8vLU0ZGRqPrbtiwQV999ZWmTJnS5M8xDEM7d+5UXFxcq8YLAACsweeHqHJycpSVlaXBgwcrPT1dL7/8sgoKCjR16lRJtYePDhw4oCVLlnist3DhQg0dOlTJyclefc6ePVtpaWnq06ePysrK9Oyzz2rnzp16/vnnfb05AADAD/g84EyaNElHjhzRnDlzVFhYqOTkZK1evdp9VVRhYaHXZ+KUlpZq5cqVeuaZZ+rts6SkRHfeeaeKiooUFRWllJQUffTRRxoyZIivNwcAAPiBc3KS8bRp0zRt2rR6H8vNzfVaFhUV1eiZ0k8//bSefvpps4YHAAAshu+iAgAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlhPU1gPAacorpKLDUkWlFBIsxUZLYSFtPSoAAPwOAae9KDos7f7Gc9l3RVLfXrVBBwAANNs5OUS1YMECJSUlKSQkRKmpqdq4cWODbT/88EPZbDav27///W+PditXrlT//v3lcDjUv39/rVq1yteb4TvlFd7hps7ub6RTFedyNAAA+D2fB5wVK1ZoxowZeuihh7Rjxw5deumlGjt2rAoKChpdb/fu3SosLHTf+vTp434sPz9fkyZNUlZWlnbt2qWsrCxNnDhRH3/8sa83xzeKDjf+eGETjwMAAA8+DzhPPfWUpkyZojvuuEMXXHCB5s2bp4SEBL3wwguNrtejRw/Fxsa6b4GBge7H5s2bp1GjRmnmzJnq16+fZs6cqSuuuELz5s3z8db4SEVl6x4HAAAefHoOTmVlpbZv364HH3zQY3lmZqY2b97c6LopKSmqqKhQ//799fDDD+vyyy93P5afn6977rnHo/3o0aMbDDhOp1NOp9N9v6ysTJLkcrnkcrlaskmNcrmqPP7f3L4DgoMUIMlWz2OGpJrgINWYOE5/UFc7M38/HRF1NAd1NAd1NEdHrmNLttmnAefw4cOqrq5WTEyMx/KYmBgVFRXVu05cXJxefvllpaamyul06rXXXtMVV1yhDz/8UJdddpkkqaioqEV9zp07V7Nnz/ZavnbtWoWFhZ3NptXLWS3VlXT9+vVyBDba3C08IFBXRMTIkGSz/RBzDMOQJH3w5Wc6+cUO08bpT/Ly8tp6CJZAHc1BHc1BHc3REetYXl7e7Lbn5Cqq01+0pdoX7jOX1enbt6/69u3rvp+enq7vvvtOf/zjH90Bp6V9zpw5Uzk5Oe77ZWVlSkhIUGZmpiIjI1u8PQ0pr6zS/Z+slySNHDlSUeHNv8S7uvioAr/6TlU1hmySAgJsks2m6h8naHiPgaaN0V+4XC7l5eVp1KhRstvtbT0cv0UdzUEdzUEdzdGR61h3BKY5fBpwoqOjFRgY6DWzUlxc7DUD05i0tDQtXbrUfT82NrZFfTocDjkcDq/ldrvd1J3DbvwQsOz2oJb1/aMYnerUSa+u3KbzQgM1dnBP2c/roaDQjv05OGb/jjoq6mgO6mgO6miOjljHlmyvT08yDg4OVmpqqtc0Wl5enjIyMprdz44dOxQXF+e+n56e7tXn2rVrW9Rne2SEOvTkf07o7l2lciXGSx083AAAcLZ8fogqJydHWVlZGjx4sNLT0/Xyyy+roKBAU6dOlVR7+OjAgQNasmSJpNorpHr16qULL7xQlZWVWrp0qVauXKmVK1e6+/z1r3+tyy67TE888YSuv/56vfnmm1q3bp02bdrk680BAAB+wOcBZ9KkSTpy5IjmzJmjwsJCJScna/Xq1UpMTJQkFRYWenwmTmVlpe677z4dOHBAoaGhuvDCC/XOO+/oqquucrfJyMjQ8uXL9fDDD+uRRx5R7969tWLFCg0dOtTXmwMAAPzAOTnJeNq0aZo2bVq9j+Xm5nrcv//++3X//fc32ef48eM1fvx4M4YHAAAshm8TBwAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlnNOAs6CBQuUlJSkkJAQpaamauPGjQ22/etf/6pRo0ape/fuioyMVHp6ut577z2PNrm5ubLZbF63iooKX28KAADwAz4POCtWrNCMGTP00EMPaceOHbr00ks1duxYFRQU1Nv+o48+0qhRo7R69Wpt375dl19+ua699lrt2LHDo11kZKQKCws9biEhIb7eHAAA4AeCfP0DnnrqKU2ZMkV33HGHJGnevHl677339MILL2ju3Lle7efNm+dx//HHH9ebb76pv//970pJSXEvt9lsio2N9enYAQCAf/JpwKmsrNT27dv14IMPeizPzMzU5s2bm9VHTU2Njh8/rq5du3osP3HihBITE1VdXa2BAwfqscce8whAp3M6nXI6ne77ZWVlkiSXyyWXy9WSTWqUy1Xl8f+W9u25vksum2Ha2PxNXe3M/P10RNTRHNTRHNTRHB25ji3ZZp8GnMOHD6u6uloxMTEey2NiYlRUVNSsPv70pz/p5MmTmjhxontZv379lJubqwEDBqisrEzPPPOMhg0bpl27dqlPnz5efcydO1ezZ8/2Wr527VqFhYW1cKsa5qyW6kq6fv16OQLPfv333lvb4vWtKC8vr62HYAnU0RzU0RzU0RwdsY7l5eXNbuvzQ1RS7eGk0xmG4bWsPm+88YZmzZqlN998Uz169HAvT0tLU1pamvv+sGHDNGjQID333HN69tlnvfqZOXOmcnJy3PfLysqUkJCgzMxMRUZGns0m1au8skr3f7JekjRy5EhFhbfsnKDT1x89OlNhwefk19MuuVwu5eXladSoUbLb7W09HL9FHc1BHc1BHc3RketYdwSmOXz6ChodHa3AwECv2Zri4mKvWZ0zrVixQlOmTNGf//xnXXnllY22DQgI0CWXXKI9e/bU+7jD4ZDD4fBabrfbTd057MYPoc1uD2px357r22W3d9yAU8fs31FHRR3NQR3NQR3N0RHr2JLt9elVVMHBwUpNTfWaRsvLy1NGRkaD673xxhu69dZb9frrr+vqq69u8ucYhqGdO3cqLi6u1WMGAAD+z+dTBDk5OcrKytLgwYOVnp6ul19+WQUFBZo6daqk2sNHBw4c0JIlSyTVhpuf//zneuaZZ5SWluae/QkNDVVUVJQkafbs2UpLS1OfPn1UVlamZ599Vjt37tTzzz/v680BAAB+wOcBZ9KkSTpy5IjmzJmjwsJCJScna/Xq1UpMTJQkFRYWenwmzksvvaSqqirddddduuuuu9zLb7nlFuXm5kqSSkpKdOedd6qoqEhRUVFKSUnRRx99pCFDhvh6cwAAgB84Jyd5TJs2TdOmTav3sbrQUufDDz9ssr+nn35aTz/9tAkjAwAAVsR3UQEAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsJausBAAA6FsMwZBhGI483su7pfZzW1pDhsZ5h/LDszHU8fsZp65/5s09f7/T73u3OGHDjdxvdvuaoqqqSJB0scyooqNr75zfyc+prWd/voiVjrK+tPcimuKjQ5nfiAwQci6iuMVThqtYpV7UqXNWqqWl9nw390ZwL1d//AX93tFwBgQ3vpk2NsLEnUTOcTe/NH1LznnTqf8Kq/beujnsOnlBgUOv+3Fv0JNrENrblvuUeQwuGUFNdW8cvC8vc+6NxxgvjmX16vhh6/rCGXii9H214nGdbwSZ/N000aPpvruHH6uq49Ztjjf5do3F1dfz2yMl2W8dOjiACDlrGWVWtisoaVVRV61RlbaA55aqW02VComlH6v6AC0sr2u0fsD+oq+Ox8koFBFprHzlXQlwudT9Rqq5hXaTiwzrUKUoVdntbDwtAE3jlaKfKK6vcAabCVa1T34eaquq2f+cLdBTdTx5X72OHJUmGPVS2k8f1o5PHtbdLtA6FR7Tx6ICWC3G51KP8uBxVVXIGBak4LMKygZ2A04Zc1TW1Aeb7IHOsvNL92D/3lynEHtiGowM6thCXS72PHZbt+/s2W+3/DEm9jx3WcUeIKoKafmHoSC8oaN9OD+x14o+XWjawE3DawL+LynSiokquM2ZjKlzVbTQiAGfqUX683uU21YacHiePqyCqa6N9dLQXFLRfZwb2Oi0N7P6EgNMGTjq9ww1wOt71tz3H9ydon+3jHfEFBb5Xd05YYlgXqayk2eeEmRHY/Q0BB2hneNffPjibuOqsqcc74gsKfKs154S1NrD7IwIO0I7wrr/9KA6LUPzxUhmSx++jbu612I9eUFo7I8iMYttr7TlhrQ3s/sh6WwS0A2f7gsC7/vajwm7X3i7R6n3ssKpqDNkk2QJsskna2yW6yaDZXl5QWjsjyIxi+9Da54bWBnZ/RMAB6tGad6yteUFoT+/6IR0Kj9DhALs+/eQrnRcaqIt6R+tYVOdmzaKZ+YJytvtja2cEmVFsP1r73FBfYA8IqP3NNiew+yMCDnCG1gSU1r4gtJd3/VZhxqGVU0F2PfmfE5KkhRedr7BmvhCY9YLSmv2xte/6mVFsP8x4bjgzsF/84x46GhlpyXAjEXBwho5+rL21AYVp5PajPRxaae0LSmv3x9a+62dGsf0w67nh9MD+aspPFBJk3c9b49vELSbE5VLP0qPqc6RYPUuPKsTlava63U8e18CD+xV/vFTdTp1U/PFSDTy4X91P1v+ibUWNBRSpNqA0xqxpZEmqqjFUXfPDNx1ZdRrZF04PBqffpNpgEFLV/L+L1qp7Qbl7V6m+jmje4a06rd0fW/uunxnF9qO+54a6L1/huaF+7J0W0paHVqyitQGFaeT2wSqHVlq7P7b2XT8ziu1La84J64iYwbGI1r5jbe07xdPHcbYzSO1BawNKcVjtE/6ZH+N4ttPIZ/OuH9Y5tNLa/bG1M4LMKLY/pz837A3njU9jmMGxiNa+YzXjBaE9nPPQWq19x9oRr1Roj6xyaMWMGZTWzggyo4gz+cu5mv7xV44mtfWhFasc4jIjoLSXF4Sz/Uh3K7DKoRWzAnNrTyztSCemonH+9Eb2nByiWrBggZKSkhQSEqLU1FRt3Lix0fYbNmxQamqqQkJCdP755+vFF1/0arNy5Ur1799fDodD/fv316pVq3w1fL/Q1odWzDrE1R4cCo/Qx93i9PK+cr1T5FRBWKR2xp7Xoj/etj7EVHfC+I9OHle8PVQ/+v6+v50wfraHPK10aMWM/REwQ3s6eb85fD6Ds2LFCs2YMUMLFizQsGHD9NJLL2ns2LH617/+pZ49e3q137dvn6666iplZ2dr6dKl+sc//qFp06ape/fuuummmyRJ+fn5mjRpkh577DGNGzdOq1at0sSJE7Vp0yYNHTq0+YOrrq69maW6WqF1b2yqaxrsO6CmRgE13l+2GWDUuNcPMGoUUHPmfEjDDoWGN/qO9VBouAJqaupZs1ZlYKC+7txN55cc8Xqn+HXnbqoMCGx0fUcTO7ajytXo+l5qahQoW22tbC1YzyQVgYF6bm/tO9YXB/5YIU1s/5la87ts7fqOqsY/0v2EPVhOP3iBjy4/ofNLjngsiz9eqq87d9PhsE5Nrn8kNFxHbEHatW2vfhQaqAG9u+topwg5g+yt/F22bH9s7b4g+ff+6KGN/66toC33x5iTZfUurzsVIuZEmb6L7FK7rKbh18BWaUGfNsMwfPq11kOHDtWgQYP0wgsvuJddcMEFuuGGGzR37lyv9g888IDeeustffnll+5lU6dO1a5du5Sfny9JmjRpksrKyvTuu++624wZM0ZdunTRG2+84dWn0+mU0+l03y8rK1NCQoJK3/lAkeFNP1ECAIC2V3byhKKuvlylpaWKjIxstK1PD1FVVlZq+/btyszM9FiemZmpzZs317tOfn6+V/vRo0dr27Ztcn0/Pd1Qm4b6nDt3rqKioty3hISEs90kAADgB3x6iOrw4cOqrq5WTEyMx/KYmBgVFRXVu05RUVG97auqqnT48GHFxcU12KahPmfOnKmcnBz3/boZHNfg/nI1kQBbyuWq0vr16zVy5EjZ7fWX97P9Jaqs8unEWZvofuqkepeVeC3fG9lZh0LDm91PRVW1pr2xS5I0f2KywhzBLR5DdY0hm6328IztLMbgz3oeL1V8+QmvE76l2mnk/4Z1UkFEVJP9nP57WPA/F5/ViaVn20efkqPq5jzV4DYccYRqT+dz9zk2NTVVOrZnh7r0SVFAANdmnI3W/F2fuf653h/NWN/M56a22h9Dqqo08MhBSfWfCrGzW4wqvj/fs5MjSP3izD9PzFVW/2Gy+pyTytSdA1DHMAyvZU21P3N5S/p0OBxyOBxey+0hIbKHhDQ++JYKdKlahuwhDtkbumLFHizZrBdwDtmDdTysk3qcPO3ywfCIszihs1qn6g6zBtlr69UMIS6XepeVyCYpKOCHfcGQ1LusRMfDOvnVyaVnqziis+LLTzR8BVFk59q6Nun030OwZD+bK2fOrg9ncLDkPNX4483cL0xRHaBqGbXbEEjAOTtn93dd//rndn9s7fqmPze10f5YYQ92X9V35ivY3i7RqggN+2GBPcj811dJ9srKZrf1aWWio6MVGBjoNbNSXFzsNQNTJzY2tt72QUFB6tatW6NtGuoT505FkL3NPiHWKp9e21qnX1psyDP8n+sriEKrXLr/J510Xmigzj9eoqMRkc26VN0ql3kDkrWemw6FR+i4I8SEN7K+59NzcIKDg5Wamqq8vDyP5Xl5ecrIyKh3nfT0dK/2a9eu1eDBg90zIg21aahPdAxW+fRaMxwKj9DO2PN0IDxC/3Wd0oHv75/LS4u7nzyuoUcKdWdSmK6OdSihvKzZl6qffpm3cdpN8r/LvAGrPTfVvZHd062HCqK6ttu/R5/PbeXk5CgrK0uDBw9Wenq6Xn75ZRUUFGjq1KmSas+POXDggJYsWSKp9oqp+fPnKycnR9nZ2crPz9fChQs9ro769a9/rcsuu0xPPPGErr/+er355ptat26dNm3a5OvNQTtmlU+vNUtFkF3fRnbW4QP/VnRiXwWcw6ns0z8vw2tKvpkf/OhP7xTRtNNn83qfLNOxwM4d5oMneW5qGz6v6qRJk3TkyBHNmTNHhYWFSk5O1urVq5WYmChJKiwsVEFBgbt9UlKSVq9erXvuuUfPP/+84uPj9eyzz7o/A0eSMjIytHz5cj388MN65JFH1Lt3b61YsaJln4GDdutsnwg5rNF+mDUl35aHPGGeuk+/HZwUVvvhcKdOKPHUiXb56be+wHNT2zgnsXHatGmaNm1avY/l5uZ6LRs+fLg+/fTTRvscP368xo8fb8bw0I605onwzHNPTsdhjXPLalPyOHtmzOb5O56b2gbzYmg3OKzRvpztCcISU/L4gZVOsG0NnpvOPZ5l0G5wWKP98JpJKy9Tz/KyZh9SYEoedZjN+wHPTefWOfmyTaA5eCJsH86cSQsMsLmfKJr7hXpcBYU6VprNq5vVfPbiKJ1/vKTZX/6KtuE/exYsz0pPhP7MrJk0puQhWWc2r7Wzmjj3eMVAu2GVJ0J/Z+ZMGlPyaE8fPHm2OFHaPxFw0G5Y4YnQCphJg9nqZvO6Hy+Vjh2UusToUESU3/xNc6K0f+KZCu2Kvz8RWgEzafCFtvzgydbi/ED/xEnGaHfqngi3lx/Tt5GdCTfnGCcIw6rO9iRhZjX9E7+VNhARYtcJZ5Wcrpq2HgpQL04QhtW05iRhZjX9EwGnDfwkpvaPobrGUIWrWqdc1TpVWe3+f4WrRtU1Z37eJXBucYIwrKK1JwnzScT+iYDThgIDbAp3BCnc4f1rqHBVe4SfuuBTWcWsDwC0hBknCTOr6X8IOO1UiD1QIfZAdT5jeXWN4TXjU/d/Jn0AWFVrvjrErJOEmdX0LwQcPxMYYFMnR5A6nTHrYxiGnFU1tWGnqtpnh7iMcxSiqquqdFjSjzqHKrCRE/iaGo/hNaHsGy2pS3ObGg10Wt/S+psaqq4K0GFJ0Z2CG61jczW0nc0fU33rtk0yb8nvrLrapsOSIkPtCvz+6p/Tx91gXU5bfuZ2ej525npntG3GGFv2t9l448b6aurHNLauzag9PBRgq7012s9p/+9+4rjOb8WH7HGScMfEb9UibDabe9bHClwul3ZJ+lGXUNmb+S4N3lwul/4t6fzunahjK7hcLu2V1C82gjq2gsvl0uovpMG9uja/juUV0tZ9kuo/f+b83jEyQkNkGD9ESMOoDZR1Yct2KkTa0fBJwlHnx+nCEMcPyw3PBnU9G4bnej+0bzyQnhn6Whvqq6qqdPhLKal7uIKCghpMnc1941HfeFr2ZsZ7aXBg21+kTcABALRfRYfrXVwXVGwHj0jnnyfP6HIGe7jUt5e0+xvvPvr2UqfOnVo/znPI9f3l7d07OQjcjSDgAADar4rK1j1eJzZaiuokFR6uXSckWIqLlkJDWj9GtEsEHABA+xUS3LrHTxca8v1sDzqCtj9IBgBAQ2KjG388ronH0WERcAAA7VdYSO35M/Xp24tDTGgQh6gAAO0b58/gLBBwAADtH+fPoIU4RAUAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACzHpwHn2LFjysrKUlRUlKKiopSVlaWSkpIG27tcLj3wwAMaMGCAwsPDFR8fr5///Of673//69FuxIgRstlsHrfJkyf7clMAAIAf8WnAufnmm7Vz506tWbNGa9as0c6dO5WVldVg+/Lycn366ad65JFH9Omnn+qvf/2r/vOf/+i6667zapudna3CwkL37aWXXvLlpgAAAD8S5KuOv/zyS61Zs0ZbtmzR0KFDJUmvvPKK0tPTtXv3bvXt29drnaioKOXl5Xkse+655zRkyBAVFBSoZ8+e7uVhYWGKjY311fABAIAf81nAyc/PV1RUlDvcSFJaWpqioqK0efPmegNOfUpLS2Wz2dS5c2eP5cuWLdPSpUsVExOjsWPH6tFHH1VERES9fTidTjmdTvf9srIySbWHxFwuVwu3rHF1/Zndb0dDHc1BHc1BHc1BHc3RkevYkm32WcApKipSjx49vJb36NFDRUVFzeqjoqJCDz74oG6++WZFRka6l//sZz9TUlKSYmNj9fnnn2vmzJnatWuX1+xPnblz52r27Nley9euXauwsLBmblHLNDQWtAx1NAd1NAd1NAd1NEdHrGN5eXmz27Y44MyaNavesHC6rVu3SpJsNpvXY4Zh1Lv8TC6XS5MnT1ZNTY0WLFjg8Vh2drb7/8nJyerTp48GDx6sTz/9VIMGDfLqa+bMmcrJyXHfLysrU0JCgjIzMz2CkxlcLpfy8vI0atQo2e12U/vuSKijOaijOaijOaijOTpyHeuOwDRHiwPO9OnTm7xiqVevXvrss8908OBBr8cOHTqkmJiYRtd3uVyaOHGi9u3bp/Xr1zcZQgYNGiS73a49e/bUG3AcDoccDofXcrvd7rOdw5d9dyTU0RzU0RzU0RzU0RwdsY4t2d4WB5zo6GhFR0c32S49PV2lpaX65JNPNGTIEEnSxx9/rNLSUmVkZDS4Xl242bNnjz744AN169atyZ/1xRdfyOVyKS4urvkbAgAALMtnl4lfcMEFGjNmjLKzs7VlyxZt2bJF2dnZuuaaazxOMO7Xr59WrVolSaqqqtL48eO1bds2LVu2TNXV1SoqKlJRUZEqKyslSXv37tWcOXO0bds2ffPNN1q9erUmTJiglJQUDRs2zFebAwAA/IhPPwdn2bJlGjBggDIzM5WZmamLLrpIr732mkeb3bt3q7S0VJK0f/9+vfXWW9q/f78GDhyouLg4923z5s2SpODgYL3//vsaPXq0+vbtq7vvvluZmZlat26dAgMDfbk5AADAT/jsKipJ6tq1q5YuXdpoG8Mw3P/v1auXx/36JCQkaMOGDaaMDwAAWBPfRQUAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACzHpwHn2LFjysrKUlRUlKKiopSVlaWSkpJG17n11ltls9k8bmlpaR5tnE6nfvWrXyk6Olrh4eG67rrrtH//fh9uCQAA8Cc+DTg333yzdu7cqTVr1mjNmjXauXOnsrKymlxvzJgxKiwsdN9Wr17t8fiMGTO0atUqLV++XJs2bdKJEyd0zTXXqLq62lebAgAA/EiQrzr+8ssvtWbNGm3ZskVDhw6VJL3yyitKT0/X7t271bdv3wbXdTgcio2Nrfex0tJSLVy4UK+99pquvPJKSdLSpUuVkJCgdevWafTo0eZvDAAA8Cs+Czj5+fmKiopyhxtJSktLU1RUlDZv3txowPnwww/Vo0cPde7cWcOHD9fvfvc79ejRQ5K0fft2uVwuZWZmutvHx8crOTlZmzdvrjfgOJ1OOZ1O9/2ysjJJksvlksvlavW2nq6uP7P77Wioozmoozmoozmoozk6ch1bss0+CzhFRUXuUHK6Hj16qKioqMH1xo4dqwkTJigxMVH79u3TI488opEjR2r79u1yOBwqKipScHCwunTp4rFeTExMg/3OnTtXs2fP9lq+du1ahYWFtXDLmicvL88n/XY01NEc1NEc1NEc1NEcHbGO5eXlzW7b4oAza9asesPC6bZu3SpJstlsXo8ZhlHv8jqTJk1y/z85OVmDBw9WYmKi3nnnHd14440NrtdYvzNnzlROTo77fllZmRISEpSZmanIyMhGt6WlXC6X8vLyNGrUKNntdlP77kioozmoozmoozmoozk6ch3rjsA0R4sDzvTp0zV58uRG2/Tq1UufffaZDh486PXYoUOHFBMT0+yfFxcXp8TERO3Zs0eSFBsbq8rKSh07dsxjFqe4uFgZGRn19uFwOORwOLyW2+12n+0cvuy7I6GO5qCO5qCO5qCO5uiIdWzJ9rY44ERHRys6OrrJdunp6SotLdUnn3yiIUOGSJI+/vhjlZaWNhhE6nPkyBF99913iouLkySlpqbKbrcrLy9PEydOlCQVFhbq888/15NPPtnSzQEAABbks8vEL7jgAo0ZM0bZ2dnasmWLtmzZouzsbF1zzTUeJxj369dPq1atkiSdOHFC9913n/Lz8/XNN9/oww8/1LXXXqvo6GiNGzdOkhQVFaUpU6bo3nvv1fvvv68dO3bo//2//6cBAwa4r6oCAAAdm89OMpakZcuW6e6773Zf8XTddddp/vz5Hm12796t0tJSSVJgYKD++c9/asmSJSopKVFcXJwuv/xyrVixQhEREe51nn76aQUFBWnixIk6deqUrrjiCuXm5iowMNCXmwMAAPyETwNO165dtXTp0kbbGIbh/n9oaKjee++9JvsNCQnRc889p+eee67VYwQAANbDd1EBAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADL8WnAOXbsmLKyshQVFaWoqChlZWWppKSk0XVsNlu9tz/84Q/uNiNGjPB6fPLkyb7cFAAA4EeCfNn5zTffrP3792vNmjWSpDvvvFNZWVn6+9//3uA6hYWFHvffffddTZkyRTfddJPH8uzsbM2ZM8d9PzQ01MSRAwAAf+azgPPll19qzZo12rJli4YOHSpJeuWVV5Senq7du3erb9++9a4XGxvrcf/NN9/U5ZdfrvPPP99jeVhYmFdbAAAAyYcBJz8/X1FRUe5wI0lpaWmKiorS5s2bGww4pzt48KDeeecdLV682OuxZcuWaenSpYqJidHYsWP16KOPKiIiot5+nE6nnE6n+35ZWZkkyeVyyeVytXTTGlXXn9n9djTU0RzU0RzU0RzU0RwduY4t2WafBZyioiL16NHDa3mPHj1UVFTUrD4WL16siIgI3XjjjR7Lf/aznykpKUmxsbH6/PPPNXPmTO3atUt5eXn19jN37lzNnj3ba/natWsVFhbWrLG0VENjQctQR3NQR3NQR3NQR3N0xDqWl5c3u22LA86sWbPqDQun27p1q6TaE4bPZBhGvcvrs2jRIv3sZz9TSEiIx/Ls7Gz3/5OTk9WnTx8NHjxYn376qQYNGuTVz8yZM5WTk+O+X1ZWpoSEBGVmZioyMrJZY2kul8ulvLw8jRo1Sna73dS+OxLqaA7qaA7qaA7qaI6OXMe6IzDN0eKAM3369CavWOrVq5c+++wzHTx40OuxQ4cOKSYmpsmfs3HjRu3evVsrVqxosu2gQYNkt9u1Z8+eegOOw+GQw+HwWm632322c/iy746EOpqDOpqDOpqDOpqjI9axJdvb4oATHR2t6OjoJtulp6ertLRUn3zyiYYMGSJJ+vjjj1VaWqqMjIwm11+4cKFSU1N18cUXN9n2iy++kMvlUlxcXNMbAAAALM9nn4NzwQUXaMyYMcrOztaWLVu0ZcsWZWdn65prrvE4wbhfv35atWqVx7plZWX685//rDvuuMOr371792rOnDnatm2bvvnmG61evVoTJkxQSkqKhg0b5qvNAQAAfsSnH/S3bNkyDRgwQJmZmcrMzNRFF12k1157zaPN7t27VVpa6rFs+fLlMgxD//M//+PVZ3BwsN5//32NHj1affv21d13363MzEytW7dOgYGBvtwcAADgJ3z6QX9du3bV0qVLG21jGIbXsjvvvFN33nlnve0TEhK0YcMGU8YHAACsie+iAgAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAluPTgPO73/1OGRkZCgsLU+fOnZu1jmEYmjVrluLj4xUaGqoRI0boiy++8GjjdDr1q1/9StHR0QoPD9d1112n/fv3+2ALAACAP/JpwKmsrNSECRP0y1/+stnrPPnkk3rqqac0f/58bd26VbGxsRo1apSOHz/ubjNjxgytWrVKy5cv16ZNm3TixAldc801qq6u9sVmAAAAPxPky85nz54tScrNzW1We8MwNG/ePD300EO68cYbJUmLFy9WTEyMXn/9df3iF79QaWmpFi5cqNdee01XXnmlJGnp0qVKSEjQunXrNHr0aJ9sCwAA8B8+DTgttW/fPhUVFSkzM9O9zOFwaPjw4dq8ebN+8YtfaPv27XK5XB5t4uPjlZycrM2bN9cbcJxOp5xOp/t+aWmpJOno0aNyuVymboPL5VJ5ebmOHDkiu91uat8dCXU0B3U0B3U0B3U0R0euY93RHMMwmmzbrgJOUVGRJCkmJsZjeUxMjL799lt3m+DgYHXp0sWrTd36Z5o7d657Nul0SUlJZgwbAACcQ8ePH1dUVFSjbVoccGbNmlVvWDjd1q1bNXjw4JZ27Waz2TzuG4bhtexMjbWZOXOmcnJy3Pdramp09OhRdevWrcl+W6qsrEwJCQn67rvvFBkZaWrfHQl1NAd1NAd1NAd1NEdHrqNhGDp+/Lji4+ObbNvigDN9+nRNnjy50Ta9evVqabeSpNjYWEm1szRxcXHu5cXFxe5ZndjYWFVWVurYsWMeszjFxcXKyMiot1+HwyGHw+GxrLlXdZ2tyMjIDrfj+QJ1NAd1NAd1NAd1NEdHrWNTMzd1WhxwoqOjFR0d3eIBNUdSUpJiY2OVl5enlJQUSbVXYm3YsEFPPPGEJCk1NVV2u115eXmaOHGiJKmwsFCff/65nnzySZ+MCwAA+BefnoNTUFCgo0ePqqCgQNXV1dq5c6ck6cc//rE6deokSerXr5/mzp2rcePGyWazacaMGXr88cfVp08f9enTR48//rjCwsJ08803S6pNblOmTNG9996rbt26qWvXrrrvvvs0YMAA91VVAACgY/NpwPntb3+rxYsXu+/Xzcp88MEHGjFihCRp9+7d7quaJOn+++/XqVOnNG3aNB07dkxDhw7V2rVrFRER4W7z9NNPKygoSBMnTtSpU6d0xRVXKDc3V4GBgb7cnGZxOBx69NFHvQ6JoWWoozmoozmoozmoozmoY/PYjOZcawUAAOBH+C4qAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcEy1YsEBJSUkKCQlRamqqNm7c2NZD8juzZs2SzWbzuNV9wjUa9tFHH+naa69VfHy8bDab/va3v3k8bhiGZs2apfj4eIWGhmrEiBH64osv2maw7VhTdbz11lu99s+0tLS2GWw7NXfuXF1yySWKiIhQjx49dMMNN2j37t0ebdgfm9acOrI/No6AY5IVK1ZoxowZeuihh7Rjxw5deumlGjt2rAoKCtp6aH7nwgsvVGFhofv2z3/+s62H1O6dPHlSF198sebPn1/v408++aSeeuopzZ8/X1u3blVsbKxGjRrl/mZe1GqqjpI0ZswYj/1z9erV53CE7d+GDRt01113acuWLcrLy1NVVZUyMzN18uRJdxv2x6Y1p44S+2OjDJhiyJAhxtSpUz2W9evXz3jwwQfbaET+6dFHHzUuvvjith6GX5NkrFq1yn2/pqbGiI2NNX7/+9+7l1VUVBhRUVHGiy++2AYj9A9n1tEwDOOWW24xrr/++jYZj78qLi42JBkbNmwwDIP98WydWUfDYH9sCjM4JqisrNT27duVmZnpsTwzM1ObN29uo1H5rz179ig+Pl5JSUmaPHmyvv7667Yekl/bt2+fioqKPPZPh8Oh4cOHs3+ehQ8//FA9evTQT37yE2VnZ6u4uLith9Su1X1SfdeuXSWxP56tM+tYh/2xYQQcExw+fFjV1dXubzyvExMTo6KiojYalX8aOnSolixZovfee0+vvPKKioqKlJGRoSNHjrT10PxW3T7I/tl6Y8eO1bJly7R+/Xr96U9/0tatWzVy5Eg5nc62Hlq7ZBiGcnJy9NOf/lTJycmS2B/PRn11lNgfm+LT76LqaGw2m8d9wzC8lqFxY8eOdf9/wIABSk9PV+/evbV48WLl5OS04cj8H/tn602aNMn9/+TkZA0ePFiJiYl65513dOONN7bhyNqn6dOn67PPPtOmTZu8HmN/bL6G6sj+2DhmcEwQHR2twMBAr3cfxcXFXu9S0DLh4eEaMGCA9uzZ09ZD8Vt1V6Gxf5ovLi5OiYmJ7J/1+NWvfqW33npLH3zwgc477zz3cvbHlmmojvVhf/REwDFBcHCwUlNTlZeX57E8Ly9PGRkZbTQqa3A6nfryyy8VFxfX1kPxW0lJSYqNjfXYPysrK7Vhwwb2z1Y6cuSIvvvuO/bP0xiGoenTp+uvf/2r1q9fr6SkJI/H2R+bp6k61of90ROHqEySk5OjrKwsDR48WOnp6Xr55ZdVUFCgqVOntvXQ/Mp9992na6+9Vj179lRxcbH+93//V2VlZbrlllvaemjt2okTJ/TVV1+57+/bt087d+5U165d1bNnT82YMUOPP/64+vTpoz59+ujxxx9XWFiYbr755jYcdfvTWB27du2qWbNm6aabblJcXJy++eYb/eY3v1F0dLTGjRvXhqNuX+666y69/vrrevPNNxUREeGeqYmKilJoaKhsNhv7YzM0VccTJ06wPzalDa/gspznn3/eSExMNIKDg41BgwZ5XM6H5pk0aZIRFxdn2O12Iz4+3rjxxhuNL774oq2H1e598MEHhiSv2y233GIYRu2luY8++qgRGxtrOBwO47LLLjP++c9/tu2g26HG6lheXm5kZmYa3bt3N+x2u9GzZ0/jlltuMQoKCtp62O1KffWTZLz66qvuNuyPTWuqjuyPTbMZhmGcy0AFAADga5yDAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALOf/Ax2rPpGcGbJrAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ACF = plot_acf(y, lags=None, alpha=0.05, title='ACF of MA(1)', color='pink')\n",
-    "plt.grid()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Eventually we plot the PSD of the MA(1) process."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Frequency')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "F, PSD = signal.periodogram(y, fs=Fs, scaling='density', return_onesided=False)\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(F, PSD, color='pink')\n",
-    "plt.title('PSD of MA(1)')\n",
-    "plt.ylabel('Power: PSD')\n",
-    "plt.xlabel('Frequency')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The PSD values seem to have larger values at lower frequencies. This indicates that lower frequencies have higher contribution to data variability (as the moving average reduces the high frequency noise). "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "mude2",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/book/time_series/figs/signal_noise.png b/book/time_series/figs/signal_noise.png
index 1fdd2a299baa8f5a305c25e5bae2cfb07dfed00d..271e640cd8353b7b086c79d5dc7a4fd9ddad9c0b 100644
Binary files a/book/time_series/figs/signal_noise.png and b/book/time_series/figs/signal_noise.png differ
diff --git a/book/time_series/figs/stat_question.png b/book/time_series/figs/stat_question.png
new file mode 100644
index 0000000000000000000000000000000000000000..6f57c0df5e32e70934a0da70f3594f010d2ac8bd
Binary files /dev/null and b/book/time_series/figs/stat_question.png differ
diff --git a/book/time_series/figs/tsa_cover.png b/book/time_series/figs/tsa_cover.png
new file mode 100644
index 0000000000000000000000000000000000000000..934a85cd2c199821c3cfe6aacaaa23c90294cc24
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diff --git a/book/time_series/fit_BLUE.ipynb b/book/time_series/fit_BLUE.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4c8555362ddaf51908e69907fc6690aafb81afa3
--- /dev/null
+++ b/book/time_series/fit_BLUE.ipynb
@@ -0,0 +1,280 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Time series modelling\n",
+    "We have a time series with a clear trend, seasonality and jump. The researcher knows that there is jump present at $t=270$ due to a change in the data collection method. First we will load the data and plot it. After that, we will use the PSD to identify the frequency of the seasonality and the trend. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Import the data\n",
+    "data = np.loadtxt(\"y_values.csv\", delimiter=\",\")\n",
+    "t = np.arange(0, len(data), 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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58+fDZrP145kTQgghhBBCCDlRvi9vxXdHW/GfHyr7+1ROOFV//vDNmzfj0ksvxYUXXggAKCkpwbvvvott27YBELLkzz77LP785z/j0ksvBQC8/fbbyM7OxieffIJrrrmm386dEEIIIYQQQsiJYbG7AQAdFkc/n8mJ169B+YwZM/Daa6+hrKwMw4YNw969e7Fp0yY8/fTTAIDKyko0NTVh7ty50muSk5Nx5pln4scffwwalNvtdtjtdulro9EIAHA6nXA6nSf4HcWOndtAPkdC6DolAx1do+RkQNcpGejoGiUDgdkmBOMdFnvAtXgyXKPRnFu/BuX33nsvjEYjRowYAaVSCbfbjUcffRTXX389AKCpSegfyM7O9nlddna29D1/jz/+OJYtWxbw+Jo1a5CQkNDH76DvrV27tr9PgZAe0XVKBjq6RsnJgK5TMtDRNUr60746DoASnRYHvvhyFRRc4HMG8jVqtVojfm6/BuUffPABVqxYgXfeeQejR4/Gnj17cOeddyIvLw8333xzTMe87777cPfdd0tfG41GFBYWYt68edDr9X116n3O6XRi7dq1OP/886FWq/v7dAgJiq5TMtDRNUpOBnSdkoGOrlEyEBxcUwbUVoEHh5lz5iI1QSN972S4RlnFdiT6NSj/wx/+gHvvvVcqQx87diyqq6vx+OOP4+abb0ZOTg4AoLm5Gbm5udLrmpubMWHChKDH1Gq10Gq1AY+r1eoB+wuTO1nOk5ze6DolAx1do+RkQNcpGejoGiX9yeH2/tlo55GVHHgtDuRrNJrz6tfp61arFQqF7ykolUp4PB4AwKBBg5CTk4NvvvlG+r7RaMTWrVsxffr0n/RcCSGEEEIIIYT8NLplUfmpPuytXzPlF198MR599FEUFRVh9OjR2L17N55++mnceuutAACO43DnnXfir3/9K4YOHYpBgwbhgQceQF5eHi677LL+PHVCCCGEEEIIISeI1UlB+U/in//8Jx544AH8+te/RktLC/Ly8rBkyRI8+OCD0nPuueceWCwW/OIXv4DBYMCsWbOwevVqxMXF9eOZE0IIIYQQQgg5UShT/hNJSkrCs88+i2effTbkcziOw8MPP4yHH374pzsxQgghhBBCCCH9ptvpkv7caT21g/J+7SknhBBCCCGEEEL8yTPl7WYKygkhhBBCCCGEkJ+MVRaUU6acEEIIIYQQQsgpwWRzYm+tob9Po0c22aC39lO8p5yCckIIIYQQQgg5TTz65WFc+uIP+GJfQ3+fSljdsqC8k4JyQgghhBBCCCGngsNNJgDAmz9U9elx39lag7OfXI/jreY+OZ71NJq+TkE5IYQQQgghhJwmOix2AMCO6k6UNZv65JjdDjeeWH0ENR1WrDvc3CfH9C1ft/fJMQcqCsoJIYQQQggh5DTRIZtk/u62mj455md769HV7QQANHbZen08p9sDp5uXvrY5PT7T2E81FJQTQgghhBBCyGnA5nTDIgtuP9pVD6fb06tj8jyPNzdXS183GnoflMv7yZUKDsCpnS2noJwQQgghhBBCTgOsN1ul4BCnVqCr24n6zu5eHXNndScONxqlrxuNvQ/KbeKNA6WCQ4ZOAwDotDh7fdyBioJyQgghhBBCCDkJHG814/lvymGyxRagsqA8LVGDgtQEAEBdL4Py/+2qBwCMzNUDABoNvTse4B3yFq9WIi1RC4Ay5YQQQgghhBBC+tk/1pbh6bVl+Hh3fUyvb/cJyuMBAHWd1pjPx+Hy4KsDjQCAX84uBQC0mu29Loln5evxGiXSEtUAgKo2S6+OOZBRUE4IIYQQQgghJwFWJl7bEVsgzSavp+u8QXl9LzLbmypaYbA6kaHTYuHYXGiUCvA80NzLEnZ5prxQzOg/9PkhPPLFIfA8H+6lJyUKygkhhBBCCCGntdoOKyoHeCbW7nKjul0IxhtinHDebmaZcm2flK9/tqcBAHDRuFyolQpkJwul5k29nMDO1qHFq5W4d8EIXDm5AADw+qZKHKg3hnvpSYmCckIIIYQQQshpy+3hsejlzbjkhU3osvZumBjP8/i+vBWdFkfPT45SZZsFbo+QJY416GXl6+mJGuSn9K583e5yY+0hYSf5xePzAAC5ycIxY71pwLD1Z/EaJVISNPj7VeNx/qhsAMDG8tZeHXsgoqCcEEIIIYQQctpqt9jRYrLDZHPhx+NtvTrWmkPNuPH1bXjws4N9dHZe5c1m6c+xBuUd5mA95bFlyhsNNlgcbsSpFZhUlAIAyEuOE8+vd8PerLJMOTN7WCYAYMNRCsoJIYQQQggh5JTRZvJmtX+oaO/VsXZUdQAAfjzW1ue9z+Ut3qC82WiTsubRkDLlOu/09SajDQ5X9IPZWkxCf3qOPg4cJ+wSz2GZ8l7uKrfJMuUMC8p31nTGPH1+oKKgnBBCCCGEEHLaajV7V239cKx3mfIjTSYAQJvZ0asBasGUN5ukP7s8PNrM0a8Ikwa9JWqQodNAqxIGszXGkNlmw9yykuKkx/JShD/Hcjw5q8MFwDcoL0xLQGlmItweHpuPdfTq+AMNBeWEEEIIIYSQ01abyRvcHm+19GpI2eFGb+C8t7arV+fFfLizDu9tq/HJlANAYwzn6d1TrgXHcb0qYWeZ8iy9VnosR8/K13vZU+4UMvfy8nXAmy3/vqJ3N08GGgrKCSGEEEIIIact/4zzDzEGfG1mu8+x9tUZenNaAICubifu+XAv7v1oPyrEoDy3F33b8j3lAGQT2KMf9tYSNFPeR4PexJ7yBE3woHxjeTtOpc1oFJQTQgghhBBCTlutYsZXqRD6omMtYT/aZPL5ek+tAQBQ1WbBrCe+xdNrjkZ9zOp2C+St4zqtChMKUwBEnyl3uDww2YSy8HQpKO99pjxblilnNwzazHas2FoNY4y9392sfN0vUz6tNB1Lzi7FE1eMxikUk1NQTgghhBBCCDl9sez2jMHpAICd1Z0xHYf1kw/KSAQAHKjvgtvD44X1Fajr7MaHO+uiPmZVu28Ge0iWTlo7Fm1Q3mkVsuRKBYfkeDUAIF8Myj/aVY/b3tweVcZc6imXBeVpiRpkJmnB88D9Hx/Ab97ZHdU5MixTHucXlMeplbhv4UhML02HeA/llEBBOSGEEEIIIeS01SauCTt3RBYAoLrdCoM1+j3jRxqNAIALx+YiQaOExeHGpoo2fLqnHoBQ0m22u6I6Zk27BYBQtn3l5AL8ft4w2TC16IJydvMhNUENhRjRloo3EOoN3fjmSAtW7gh/44DneRxtMsHp9ngz5bLydY7j8MnSmbhz7lAAwOZjbdLQtmhYHcHL109VFJQTQgghhBBCTlusfH1wpk7Kcu+ti35IG8uUj87TY2x+MgDg1//dCafbW2h9zG9YW0+qxUz55OJU/P2q8ThraCZyYuwp7/DrJweA80Zm4/fnD8PZYq/28TZLwOv+/f1x3PSfbbA53fhsbwPmP7sRz64rk2XK43yen58Sj9+dNxT5KfFwunlsr4q+8sDmDFyJdiqjoJwQQgghhBBy2mIZ5MwkLcYVCMH0PrEfPFJuD48ycWXZ8Jwk/G7uUOjjVLCIGd+kOBUABExQ70l1hxCUF6cnSI+xvu1od4FXiQG3PChXKxX4zXlDceO0YgDA8dbA83vjhypsLGvFzupOabr82kPNUn+6vHyd4TgOs4ZkAAgcnOdye7DleHvQqgGL3YUD9V3odgQvXz9Vqfr7BAghhBBCCCGkP7jcHnSIpeoZOi3GF6Tg0z0N2Bvl5PSKFjPsLg8SNEoUpyeiNFOHDX+Yg9e+Pw61UoFOiwPLt1SjvMXU88FkqsXy9aI0b1CeI/aUNxtt8Hh4qRQ9nBfXV+Af4qC5kbn6gO+XZgoVApVtFvA8D47zHpNl2NvMdrSLNzDKmoXgPV6tRJI2eEg5Y0g63t9Ri03lvkH5Wz9W45EvDiFJq8IlE/IwriAZk4vTEKdW4IZ/b/Xpoz9dytcpKCeEEEIIIeQnZne5YXN6pIFbA8m3R1uxu53Dwl4ep97QjZte34pbZw3C9WcW98m59bUOiwM8Dyg4IYM8Xpxsvqe2KyA4DWd3jVCiPa4gWZrinpqowR8vGAEAWP5jFQCgornnTHlFixl3f7AHt80ahGajEASXpCdK389O0kLBAS4PjzazPaB83N/O6k489bUQkF8+MR93nz8s4DmFqQlQKjhYHW40G+1SibzN6ZaGrrWa7NJKNSZLrw35Gc0YLGTKDzUa0W62I12nld4fAJjsLqzYWoMVW4Xna1QKOFwen2P4T18/VVH5OiGEEEIIIT+xy17cjNlPrY9pP/SJ5HR78Nv39uKtMoU0rTtW3x5pwbFWC55dVw63Z2AusGoVM79piVooFRxG5+mhUnBoM9ujGqS2u8YAAJhYlBr0+0OykgBEVr7++qZK7Kvrwv0fHwAglL6nJHhv3qiUCuSIgXhtBKvM/v39cQDAFZPy8czVE5AUF3gjSKNSSNl4eQm7/BpoMzsCdrrLh7z5y0zSYkSO8L43H2uXHmc9/D+bUoDFM0swY3A61EoODrHSQI56ygkhhBBCCCF9zun24HCjEQarE38SA6+BotPqgN3lAQ8OtR2+AZ/T7cGjXx7C6gNNER3LIGZVW012bK/q6PNz7Qts8nqGTuizjlMrMVwMJPdG0VfOdpJPFDPt/oZm6wAAtZ1WqV9artvhRl2nFW4Pj7WHmgFA6rkuTk8IyEYXiT3mNR2Bg9nkatqt+Pqg8PtacvbgsM9lk9iPtVnw7rYa7K01SKXrACtf971Rkxmkn1yOZcu3VsqCcjGwnzsyG3+5eDTeuX0adtx/Pv590xT871czfF5PmXJCCCGEEEIIXG4PXvquAvui7DMOhQ3IAoCNZa3YVjlwAtZOi1P6c73BNyhfc7AZ//q+Eo98cSiiY3XIsqxf7mvsmxPsYyxrm5nkDS7Z5PTD4oqznphsTpSJveITilKCPic9UYPUBDV4HjgWZJjab9/bjbOeXI+n1x4NyEYXpyUGPJ9ltWvaw2fK//NDJTw8cPawTOlmQyhs8vxrG4/hvo/244//2weD1Xs9tJntUWXKAeCMQULlwA7ZBPa2IJ95coIac0dlY0ROklQFAFCmnBBCCCGEEAJgQ1krnlx9NOJgtCcmm9Pn639+W94nx+0L7RZv0FXvN9372yMtAIAmoy2icnR5QPfVgSa4PTw6LQ78+/vjUg92tFpMNmkVV2+YbE48vuow1ovvKVPnDRAHZwpZ7WNB1oMFs6+uCzwPFKTGIytEkMpxHIaKJezBgvK1h5rB88CL648B8B3sJp+87n1MCKCre8iUbyxvBQBpuno4peL7ZhUSNR1Wn0x5TbsVdr+e7+weMuVTStIAAEebTeiyOsHzfNAbIQzHcZg6KE36OkF9eoxAo6CcEEIIIYSQMGrFtVT1EfTvRkKeKQe8u6gHAnmmvMHQjYoWMx754hCaumz47qgQwLo93sAqHP/S56te2Yyzn1yPv355GFe98iM+2FEb1bk53R5c8s8fcOHzm2AJsk4rGp/srserG4/jy/1CBj9DFiCySeTHWyMLylnp+oQQpev+x630C/Z5PvAGxz0XDJdWn8mHvDGFYtDOrs1g3B5e+v7I3PBZcvn5MVaHG7WymQdV4iT4BI0S+SnCBPhg69DkMnRalGYkgueBnTUdMHa74HB7pO8FM7XE25cfpzk9wtXT410SQgghhBASo2YxAG0124MGUNEyiplyNqW7twPV+lKHX6b8uW/K8fqmSlzz2o8+k7cbunq+QWEQ3xcL9nbVGGCyu5Ch08Dl4XHPh/uwTuyfjkSb2Y4mow1tZju2HG/v+QVhHPMLuOW7u1kZd1WbBR5ZRUCo6oCehrwxLDPs35dt9esxj1MrMGd4Fh67fCwunZCHheNyA45VLAbl4W7oNBi64XTz0CgVyBXXqIXDesrljjZ5V7ixt5+h0+KWGSUYmqWTesbDmSpmy7dVdqLVLFQ56ONUIXeQjy9Ikf58uvSUnx71AIQQQgghhMSoWZzC7XTzMFidSJUFcLEwdgtZ3qK0BFS2WWCyueB0e6BW9n++rMMvU+5wC0FflV/w12iwAUU9HEsMyp9cNA4alQIVLWakJmhw9rBM3PX+Hny2twGbKtowd1R2ROcmD2Y3lLXivJHBXxfJ7m62/5sZIeu3LkxLgErBodvpRpPRhryUeHy5rxFL39mFF6+bhAv9guQGsffeP9PsjwX+HX5rxdjXWpUCT145Dhk6LRK1KswZkYU5I7KCHouVt7eY7Oh2uIP2XrOAvTAtXroBFE5mkhYZOg3azA5oVQrYXR6foJxJ12lw+9mluP3s0h6PCQBTSlLx/o5a7KjqwNnDMqSfFcrY/GTMHpYJBQfoQuxAP9X0///yCSGEEEIIGcCaTd4e5lZzz2XbPWE95QWp8WBDteX91/1Jnimv7rCi0i94jVML4UNjBJlyVgqflqjBuIIUXDGpAHNGZEGp4HBmqZA9DVd+HXhuvkF5MJ/srsfov3zdYwaeBayv3zwFH/16BmYPy5S+p1YqpOnmrIT9i30NAIA1hwInz7PKh552zrM93f7D0tj7SkvU4NIJ+Zg5pOfsc0qCGkliwFobYq0eKzcPVv4eDMdxeO2mKXj1xsmYJGb9g/W/pyeGL1n3d4bYI76vrgt1YgtIuKBcoeDw1q1n4I3FZ0S8J/5kR0E5IYQQQgghYTTJ9lW3GPsiKBcy5cnxaimQGygl7B2ymwPdTg94XihXnlqSimy9FpeMzwOAHnd4O1weaaVXakJgZUFBqtgTHcWednlQXt1uRVWbBU63B5/uqccqsTf8f7vq0O1044djbSGP4/bw0s8dkavHpKLUgOCvNEMYena8TQhKDzYIk9grguwZ7+qOMCjvIVOeFkUFBsdx3rVoIUrYa8QbHsURBuUAMKkoFfNH50i94k53YMk+Wx8XqaK0BGTotHC4PdJcgsweprafbk6PegBCCCGEEEJi1CwLxFlPbG+woDwpTo20BA0MVic6LQ5sq+zAsVYzrj2jh7rwCG0oa8VfPj2Ax68Yh+mD0yN6Tacl8ObAmHw9Xr95KjgAb26uAtBzppz1kys4QB8kWC1MFXqcazu6wfN8RBlR/wzzQ58fRFWbRSqt//yOWdLqLf/AFxB6xA81GjE2P1nqtZav35IrzUwEDguZcqPNKQW4x1stPuXxbg/vc5MlnFDl6+0xBOWAEOwebDCiOkS1QZU4UC7Y9PaeZIf4XIDQA9pC4TgO4wuS8c2RFnxfJtwsyYzyGKc6ypQTQgghhJBTis3pxq6azojWdvXEbHdJGV+grzLlQmZVH6dCSoI3U37X+3tw30f7g2ZjY/HZngZUtVvx9cHAkmu52g4rrnx5M1btb/QZ5saMztNDqeCgUHDISxGCtQZD+JsTnWLGPSVBE7SfOV8s3e92uoP+TLeHx/+t3Iu/f31UeowFsxqVEMJ8d7TVp9f9b6sPo9vp9nmu3H0f7cevV+zCf36oBBC+15oNPTveZsHhBu++8m6nG42ylWzy9Xb6uMgy5Z1Wh8+1yVoG0qMNytODT2DfV2dAvaFbKtGPJSjP8isvl9+8SI8yUw4AY8Td7ybxf0vhytdPR5QpJ4QQQgghp5QnVx/Ff36oxNSSVPzjqglS8BIL/53YkawC6wnLrOrj1VJ2tMVklyaat5ntGJKl6/XPqTcIQVlP57zmUDN2VHdCoeCkTHmqhkenQwhYR+UmS8/NEad4N/VQvs6CYnbTwZ9WpUR2UhyajDbUdlgDsq+HGoz4cGcdAODmGSXITNJKx7z+zCLUdXZDp1XhjEFpcLk9eODTg/ihwjuR3X/COeDtj35/u7CKLVyvNdvZfbzVjEONRp/vHWsxSyvBWOl6vFop3SwIhQ0I9PBCJQHrMWc3JaIdIFgkTWD39v1XtJhw+UubkZ2klQbtRdpTLuefKR+arUOT+L+F9Biy3GPzk32+pqDcF2XKCSGEEELIKWWz2E+8vaoTl764SSqljkWzX/ApH/QW63o0k10I5JLiVEgR+62PNJnADue/xzwSHRYHPtxZB6e4AxoA6sWp4C2m8AF0ixhsHWsxS4Fcoc773kbl6aU/54m7s1tMNrhkP8sf65FPC9JPzhSmiSXsQfa/H2nyBsI/VAi/zzYx0B6cqcO/bpqCZ66egGvPKMIl4/Oh8st4+2fKXW6PVP7OVpCF67Vma9HqDd3YJa48Y+TDz9gk/Z5K1wFhgBx7nvz82I2QaDPlQ7OEifE/HGvHYfHGweoDTXB7eDR02WBzeqBUcMhP7Xkdmj//TLn8JlFGDNsHxhZQUB4OBeWEEEIIIeSU0e1wo1ws/85K0qLT6sS3R1piPl6zX0DbYrTj870NuOqVzRjxwGr8+ZP9UR+TBXJJcSopU35IViJttkc/if3BTw/g/1buxYot1QCE8u9GscS8pYdMOcuAtlsccLiEQLswUQjKEzRKaSc2IPQTq5UcPLx3f3swLCgPl/0tFIe91QUZ9nZEtorr+3IhKA9V5p2coA7ome+wOHxumrSa7fDvZghX1p2h0yA9UQOeB1YfEIbIscBUHpRHOuSNYecuL9n3DnqLLlCdUpyKOcMz4XB5sPSdXbDYXVh32PdaL0iNj2nVnjxTrlRwPjvMM2IIqLP1cT6BOPWU+6KgnBBCCCGEnDIONRrh9vDITNLi6qmFAIB1h5vRZrbjxfUVaI9ypVlTl/B81lPb0NWNP/5vH7ZXdcLu8uDrg+FXbwXD+pCTtGppMrl8H3S0mXK7y4314o2HzceEEu5mow0uMQptMdrDZvX9S/S1KgWKxbXdE4tSfHZ+KxScFLA1hRn2JpXBhyhfB4CCNNYT3XOmnOd5KXgNVj69YIywOzwpTujOdbg9PrMAgpXbhwvKOY7Db88bCsA7gZxNnpf3/LOgXB8fWVcw68eWl9fHOuhNoeDwj59NQI4+DsdbLfj9B3uxp9YAwLvfuygtttYNNn0dEH6H8oA62ow+Iy9hp0y5LwrKCSGEEELIKWNfnQEAMC4/GXNHZgMANhxtxVlPrMdTXx/FvzdVRnU8FrCOyRdKuKvbrbA63NJ+8U6/jGwkvNPXVVLQygaUyb8fqa3HO2ARS7J31XSC53mpdJ0dWx6g+vMfXpeWqMEwPY8Xrx2PJ68cH/D83OSeh72xQW/hM+VCWXXQTHmj9yZFk9GGY63msMHrZRPzcPnEfCy7ZDTi1UoAQrn7Hz/ch+U/VvlM0Gd66rW+aXqxtL9cH6fC2eKfj7V6e7ijzZR7J7B7z8d7syH6YDctUYNnrp4AAFgtDvQbV5CM22YNkv4ciwSNStqDnpKgkXr+FRyklotosWFvCi76GxCnOgrKCSGEEELIgBRLz/b+ui4AwLiCFIzNT0ZmkhYWh1sKelnQHikWlI/NT/F5/BwxQHN5eGmidKSMspVowYLWaIPybw57s/VtZgeq260BgW6oEnae56XydSY1QQ2OA+aNypYGmsnlisPewq1F82bKQwdfbFd5RYsZf/5kPz7YIQxgazXZ0W5xgOOAycWpAIBvj7RIn0uwTG2CRoVnrp6AKyYVSAHf+iMteH9HLf721REpqz8iJwkcJwxm66nXmuM4PHXVOMwcko5fzxkila+3muxSMO7NlEcalPsOdwO8QXm4zyqc6YPTcd2Z3jV6c0dm43fnDcXy287A0jlDYjom4M2WpyVoUJKRCLWSw5AsXciJ9T1hmfJ0nTbmY5yq+j0or6+vxw033ID09HTEx8dj7Nix2LFjh/R9nufx4IMPIjc3F/Hx8Zg7dy7Ky8v78YwJIYQQQsiJ9n8r9+KsJ9fDaIuuv3pfPQvKk6FQcJg7Msvn+6ysN1IsYB2WrYNG1pt7zvAsJGiEjGxHkEnfcjanG1aHN9CWVqLFq4IGYtH0lPM8L/URs+nfO6s7Ue83PC3UKjez3SUNPmN6ymLmRrAWLZpBb41dNvx3Sw3++L99OFDfJZWuD0pPlKodvtwn9HUrFVzE+8B31Qg7yy0ON3aLZd3TStPx8vWT8a+bpkTUa52VFIcVP5+GX84eDJ1WJVUJPL7qMOwut3R9RttT3miw4bfv7sarG46FvdkQqXsXjEBechyUCg4LxuRAoeBw1tBMJGhiX7bF2hRSEtTI0Gnx1e/OwoqfT4v5eLOGZGD2sEwpi0+8+nUlWmdnJ2bOnIk5c+bgq6++QmZmJsrLy5Gamio958knn8Tzzz+Pt956C4MGDcIDDzyA+fPn49ChQ4iLC73UnhBCCCGEnHj/3VKNihYzHrxolE/vcW/wPI9V+xthdbixr7YLs4ZmRPQ6s90lDeFipbLzRuXg3W210nNYWXWkWDCbnSwMqmJl4dNK05GWqIHV0Y0OqwMlCF4K7XB5cN4/NgAAPv/NLOi0KtjFYWpJcWqkJboDXtNTprzFZMNnexpQ19mNqnYL6g3d0KoU+NmUQizfUo0d1Z0A+IDXBBOsrDtcHzgA5EWwFq0jgvL13OR4qBSc1PvO88BDnx3EvNFCID4iNwlTS4S4YK9YAZGaoOnxOmNBOeuvBrwT3LP0WlwwJifs68P53XlDcd/H+/He9lp0dTul9xdxUC6WqH+xrwEWhxuf72sAENnNhnD0cWp8csdMtJrsGJqdFPNx5FhQzj7PIVm9O268Rom3bj2j1+d1KurXoPyJJ55AYWEh3njjDemxQYO8d054nsezzz6LP//5z7j00ksBAG+//Tays7PxySef4Jprrgk4pt1uh93u/cvFaBTutDmdTjid0U+y/KmwcxvI50gIXadkoKNrlJwMTqXr1GJ3YdnnB+F087hobDbGR9G/WtFiRlFaQtDdzsZup5S9rWoz4cySyI67t6YDPC/0PKfEKeB0OjGzNAWPXTYKhm4nnvy6HJ0We8SfvcfDS8FsRoIKGToN6g3dSE1QoyRVi9QENeo6u9Fq7IbTGXyveGWrRQrkH/h4Px64aIT0Pa2Ch04T7P07wp7j/R/tx1q/KdvnjcjE9EGpQlBe1Y6sJCGgUnDCXuwmgzXoMes7hJsY6YkaqaQ6RRxaFuocMhOF4LEhxDEBoEMcqJek4cK+l6klqdhb14VHLhmFBz47hB3VndKNlSGZiRieleATuKclqHv8/aWK518nqxZg69QyE3t+fTiLJuYiJU6JX76zB2sONeO8EUIbQ6JGEdFxk+OE6go2A4B1aKTEq+F2u+AOvEcTsdQ4JVLjEvrs75YSsZKhICVuwP19dTL8PRrNufVrUP7ZZ59h/vz5uOqqq7Bhwwbk5+fj17/+NW6//XYAQGVlJZqamjB37lzpNcnJyTjzzDPx448/Bg3KH3/8cSxbtizg8TVr1iAhIbbpgz+ltWvX9vcpENIjuk7JQEfXKDkZnArX6cFODk63EGR8+s1m1GdG1gN+zAg8f1CFwkQed491wz/x2WAB2H+mbthxAEkt+yI67qYmDoAS6QorVq1aJT2eCKBTPGZzp9nne+EY7IDTrYICPHZs+hYeiwKAAoVxdqxe/RVc4tff/bgDtmPB3/uhTuGcAODLA03gjPUAlNAqeXy9+isIg719/5O8pqE17DnuOq4EwOHMTA+KdDzS44AhifVoL6sHoEJ5iwVNnWYAHHLjedRbOWzddwQ5XYcCjrW9VfzMVDbYVBwsLg7tDdVAQehrtNYsnHNVS1fI82wzCue4b8ePaD4Y8q3gqizg0nRAVb8b8/M4fFyllKoZrPVl+HbtUeTFK1FjES4S3mbs8fdnaBF+L8FUHd6LVQ17wr6+Jx4eUHNKOD3ArmPNADhUlR3Cqs4wb1RUYfBeD3Jqjz3i6/KnkucGbh/OIbvrMFatOtzfpxPUQP571GoNHGAYSr8G5cePH8fLL7+Mu+++G3/605+wfft2/Pa3v4VGo8HNN9+MpiZhgmB2drbP67Kzs6Xv+bvvvvtw9913S18bjUYUFhZi3rx50Ov1J+7N9JLT6cTatWtx/vnnQ62OvXSFkBOJrlMy0NE1Sk4Gp9J1uvPLIwBqAACpRcOwcM7giF73xuZq4OBR1Fo41CeNxJKzfXtMvytrBfbtBgBo0/KwcOG4iI67e9URoLIGZ44ahIUXDPf5XmOXDU/t24hujwILFswDx/Vcar+zuhPYtR15KfG4+MKz0ZRcif1fl+Pn50/AgjE5WG/dj8OGRhQOGYGFIfpkO7bWAEeOQKng4Pbw+K5ZC8CF1MQ4LFw4GwCwbO+36Or2lqyrE5KwcOEMAMCBeiN21RpwwxmFUCg4OFwe3LVlHQDgmcVzAlZL/a9lK/bUdsHkFN7f2aML8e72OiRl5mPhwrEB51e7sRKoKMfoQflINtiwvaoTZ4wbBXQcDHmNtpvt+Pv+DTC5OMydd0FAtYPT7cHvfhTO8bIFcyMeYLYQwHW1Bqw73AqXx4Pfnz8UKqUCO/kjeHuLcJ0NK+r5eqjdWInvGoPPoLpk7tkozQw/dT0SLxz7AcdaLWizC5/zWWdOxnkjsnp4FTCo0YSXDv8Y8HhRdhoWLpza6/M6XZwMf4+yiu1I9GtQ7vF4MGXKFDz22GMAgIkTJ+LAgQN45ZVXcPPNN8d0TK1WC602cO+dWq0esL8wuZPlPMnpja5TMtDRNUpOBj/VdVrTbsUne+px8/QSJPfQKxytH8Sd2ABQ12mL+P043N6s8rPfVOBAgwnnj8rGoskFAIAWs7fss6Er8uNWifuuh2TrA16TqRcCR6ebh4NXQBfBAKwmk3AeBWkJUKvVWDJ7KC6bWIg8cSJ5ulgi3tXtDnmODeKe81lDMrChrFVaTaaP9/7+UxM0PkG52eE93kNfHsHeWgNKMnQ4b2Q2qjvN8PBAokaJ3NTEgJsL9184Cle9IgR9HAdMLErDu9vr0GZxBD3HNovwHnNSEnDpxAK8u60G80bnYPv3B0Neo9kpKmiUCjjcHnR0u1GY5vvf3tWdwjqzOLUC6UkJUU3anlqaiamlmT6PTRmULgXlGUnaHq+HTH3ouVMF6Tqo1b0PgYrTE31Wo6UnxUd0neakeCt3R+bq0Wy0ocPiiOh9kUAD+d/7aM6rX6ev5+bmYtSoUT6PjRw5EjU1wv/ocnKEIQzNzc0+z2lubpa+RwghhBBCQnt2XRmeXluG+z4WSsAP1HdJK5h6o97Q7ROUVLVbwjzbF1sjBQgrxVYfbMLvV+6V1o81yqZ613aGXrvlr7JNOIdBGYGZ0HiNEloxo9sZ4ftna8UKxdVdSgUnBeSAfOd06OPVdAjHOGd4ps/k96Q473+w+w8Lkw96axaHqX1fLgwqY++xJCMwIAeAqSVpuHBcLgAgRx8nna98+rpFtsKNfebZSVqcMzwLr944JSD77o/jOOSIU8gbgwx721YpTD2fUJjSJ6uvJhamSH9mK8XCkT8nUaOU2iOStCokRjl9P5SiNN+22EiHtMkH3501NEPKrrMZAOT01K9B+cyZM3H06FGfx8rKylBcXAxAGPqWk5ODb775Rvq+0WjE1q1bMX369J/0XAkhhBBCTkZlLULWctX+Jvz8rR246J+bcOub23t93E3lrQC8wUh1e+T9kywo//35w/DO7WciQycEUSwIbpDtv2412WFz9jz9yuHyoFYMgEuDBOWAdw+0IcIJ7GxQGNun7Y8F5Wz9l5zJ5gTP86gRs/fF6QkYJxuEp4/zBodsbdiwbGFYnNnukna0G7qFY28+JgTlVWFuPDB/WjgSI3KScNWUQmnXNNtTvvZQM8Y89DXe2lwFwBuUsyA7UrlSUB5402RHVQcA4IyStKiOGUpBarx0o4BNLw9HvtKtNFMnBdDZUb7HcPyDcn1cZEG5WqmQ/jczfXA6fj9vOK47swi3zCjps3MjJ59+DcrvuusubNmyBY899hgqKirwzjvv4LXXXsPSpUsBCHfh7rzzTvz1r3/FZ599hv379+Omm25CXl4eLrvssv48dUIIIYQQid3lxs7qTmn/dF/ZW2vAbW9ul/Y2R4vneVTKstnrDgvVh3tqDVIAGwuPh8eKrUJl48+mCCXn7RZHxO+fBeUpCWrMGJyBknQhwGkSS739V23VRZAtr+mwSmXdoTK9KWL5frAgOhhvUB4f9PssyG/3y5RvKGvFuGVr8MK3FagTP+eitASMK0iRniPPlKeIx2GrrNweHt1ON2xON2xOYX1aWbMZLSYbKtt7DsrzU+Kx+s6zcff5w5AlfhZd3U7YnG5srxIm1G8TA2e2Ei0rTMl3MCwDH2xXOTv2lD4KyjmOw/mjssFx3lV34cj3fRelJ2BIlnCzIyfK9xhOrJlyAFg6ZzAuGpeLmYMzkJMch8cuH4uSML9Pcurr16B86tSp+Pjjj/Huu+9izJgxeOSRR/Dss8/i+uuvl55zzz334De/+Q1+8YtfYOrUqTCbzVi9ejXtKCeEEELIgPHi+mNY9PJmTH5kHe58bzfcnsimkPfk0S8P45sjLbj7/b0xHbPFZIfFIUw3H5yZCI1KgXwxmGIBeiz+t6sO++q6kKRVYcnswVIQFGm2nAXlejGQYRnMJla+HhCU93xcqXQ9M3hZN+ANojssDtz30T68/WNV2GOynxsqKJcy5X5B+b82HgfPA/9YWwaTWCpekJqACYXegDJJlimfPzobeclxuGR8nlTubba5AjL6Px5rl26ylKRHFsQlx6ulQWytJjtaxM+4zWT3WfmWHWXAyjLrTX6Z8saubtR1dkPBAZOKU6M6ZjiPXDoG2++fiwmyUvZQ0mTZ9KK0BGm/drTVAOEUpXuDco1SgTh15GHVL84ejBeumxR0HSA5PfX7lXDRRRdh//79sNlsOHz4sLQOjeE4Dg8//DCamppgs9mwbt06DBs2rJ/OlhBCCCEk0A8VQmmxw+3BJ3saYs5sA8ADnxzA9f/egp3VnVLG8VCjEe9sq4n6WMfFAK4wLQFf/OYs7PzzXCyeWQIg9qDcbHfhya+F9sPfnDcEGTotisUAJfKgXAhUWXaRZTCbjTbwPI8Gca/3cDFzHElfeWWbsNt6UEbwfeEAkCru195Q1op3t9XiH2vKQj7X7eGl/eIFaeHL1/17yuUBGwBk67WIUysxXhZQyjPl80bnYPN952FaabrUd260uaTSdeaHijapdz/SzCrHcVK2vMVkk8rY2y0OdFodcIpD97J66CP3lycGuA1+N1C2Vwn95KPy9D499L2lVHBSm0NPkrQqqJXCzY3itARcd0YRLh6f16cl4oWylgZ9vDqiaf6EhNLvQTkhhBBCyMnM4+FxuFEIwlkgEOsgNY+Hx/It1fihoh2LXt4MAEgVS67//vVRnwFpkZAPPovXKJEUp8b5o4RVs1uPd0R9PABYf6QFrSY7CtPiccsMYQ0Yy9pGOuzNKP5c/6C8qcuGTqsTdpdQsj2lRMi0RpUpDxOsJscLQfSOauFmR1e3Ey63J+hzW0w2ON08VAoO2SECVhaUG20uOEMcB/CWOufo46TSenmmXI4Fsma7C50W39/P+qOtUhVBuPfpL1c2lI0F5W1mu1SZkKHTQK2MLizITY4Xj+l7w2R7pfDZTu2j0vVYcByHdHHYW3F6IorSE/DPaydGVPoeqXhZm4Q+vl8XWpFTAAXlhBBCCCG9UNVugdXhRpxagYmFQhAZa1Aun7rNPP2zCSjNSERXtxPfRJndZtnjUln2uDg9EcOydXB5eHy4sy7qczzQ0AUAOHtoplR+WywG5dURBuVdfkG5vHydZckzdBqUZgrnXdfRc6acVQWEGvIGeG9w1MqOZwhxY4L1k+emxEEVImBNjleDJUjlfepmv99joRiUcxyHKWJJd6jMNAvWTTYnusRM+ahcPTJ0WrSKAXVyvFp6L5Hw9n93S+XrBqsT9eJ7jGXyd26KGOgb/DPl/R+UA8Bd5w/FokkFmFrSdyX0/tjNlmj6yQkJhoJyQgghhJBeONggZMmH5+iRkRTddG9//uXKJekJmD0sExeNzwMArDkYbVDu7bOWu0Q83iNfHMKDnx6QJn1H4pD4fkfnebOOJRlCcFIVQfm6y+2RdnWzYCY32ZspZ5ng3OR4FIq93H2VKWc95XKhfldSP3lK8NJ1QCipZseUZ7XNdr+gXFbqfP+FI7HsktG4aFxe0GOyoFzeU56bHIdnr54g3QAItQ4tFJbVrmyzwii7YXC4UZjMH0uvNTtmu8UhTcfv6nbiaLNwzCknMBiOxNVTi/CPn40PeUOlL1BQTvoKBeWEEEIIIb1wqJEFqXppinak0739sSAsRx+H56+diH/fPBUKBYd5Ysn5hrLWiNaDMcfbgmePl8wejCWzSwEAb/9YjW+PtER0PJ7npaB8VJ5eepxlgusimOguDwr1/uXrRptUDp2b7N2x7d+37M9sd0ll2eF6rVOCZJcNIX5XLDsfasgbwzLW7RbvHnD/THmxrMe8IDUBN88oQbxGGfR4rNfcZHOh08qm1Gswa2gGfjNnCADfvd2RyBOz2vvqDD6PHxSrHrL10fWTA8L7Znvf2Vq1XdWd4HnhZtLpsHebBeUpFJSTXqKgnBBCCCGkFw42eIPytITg07gjxUqpUxM1uGR8nrTKaXSeHnnJceh2urGpvC2iYzndHtSImWv/7LFaqcB9C0ZKg68+29sQ0TFbTHa0WxxQcMCInCTpcTa5u9UsTPQOh5WuJ2qUUh8z26XtcHmk/vy8lHgpiO6p953t7s7QacJmLYNlyjtDZsrD7yhnvBPYvccxiqvhFs8swRWT8rFgTG7YY8ixnnKT3TvojX0Od50/DJ/fMQt/vGBExMcDvFntI00mn8fZtRtLAM1xnDTJn31Wfb0KbaBbNKkAc0dm44Zpxf19KuQkR0E5IYQQQkgvSJnjXL1sD3aM5eti1tY/88ZxHOaNzgEArDnUFNGx6jq74fLwiFMrQu5nvmSCUEK99lAzuh09Z+BZZnVwpg5xam+mN1Ociu108z1WCXh3lHsDZK1KKQW36w4LWfvSzEQpk+5wecJWCByPoHQd8E5flwt1vqwCgpXmh8LO+7fv7cYN/94Ki90lla9fPD4PT/9sQsiseDDynnKDGOiz64HjOIwtSI7qeIA3U+6/Vo9Nl491VVip2BZxrFWYXbBDDMrPOE2C8qL0BPz75imnzU0IcuJQUE4IIYQQEqMWow1tZruYOdZ7s6Yxlq8bpYA1MHhkU9PXH22NqAe8XOztLUlPhEIRvP94YmEKClLjYXW4IyphPySrCpDTqBTSe28x2XGwoQvvbasJep7+O8oZKdsulqHPGJwBnUYFdurGMNlytru7p6A8JWhPeeDvqtPikAbaTStND3tMln13e3hsqmjDjupOKShPimElmE7eU84y5YmB5x2NvOTwJfixlK8DkPZ/lzebYXO6sbdW+Mz6u5+ckJMNBeWEEEIIITFi2dTSTB3iNUrv0K9e9pQHC8onF6dCpeDQarL32GMNAN+VtQIAJhaFDpA4jsPF4tC3zyMoYT8YpJ+c8e7CtuMPK/fh3o/2Y2d1Z8DzvJPXfQPWHFlgmKOPw+BM4WYCC97DlbBHsqMciLx8ffOxdvA8MCxbJ90sCKXUb4ieweqQesp1IdaehaMP1lPey57llAQ14tSh/7M/1v7voWJ7RXmLCfvru+Bwe5Ch00S1ro0QQkE5IYQQQggAYXp3tCvH2BouFpykBukvjoZBClgDg8c4tRIjcoXM5J4aQ9jjeDw81h4S3sv80dlhn7tgjFAWv6Wyvcfz8w61C9z3zHY2NxttUjlzRYs54Hn+69CYHFk2d+aQDGm6OAtSWZ92MJFMXmc/kw0t14j97MEy5ZsqhBsas4Zkhj0eANwyYxD+e9uZmDNceG5jlw0usUxcF0umXLanvCvMTZpocBwnDc0DAjP4sZavD80WrvuKFjO2HheunynFaVFNhieEUFBOCCGEkJNEvaEb/1hzFC2mnrPEsVi6Yhdue2sHjvoNwwqnStzLzSZ+p0o95X2fKQeACeLU7T21gRloud21BrSa7EjSqjBjcEbY57JgravbGdBzLFfRYkJ1uxUKLrB8HfBmWw81GGF3eQAANUGmsRtDBeWyjPTMId6S8eQeMuU8z3unzGeGD8qVCk4K8sfkC+/B/wYKz/P4XhymN2to+NJ1QCjdnzU0Q/oca8X3zHFAoib6oJz1lBttTql8PViGP1ryEvaRud7fn0rBSQMKozVY3CPfZnbgi32NAIAZQ3r+zAghvigoJ4QQQshJ4fl15fjntxV4Z2tNnxzP6fbglQ3HsOV4O5xuD8rEHuxIdmIz/hla1rNsdbijWl3GdHUHH/TGjC9IAQDsqTWEPQ4bBnfOiCxoVOH/c48FvTwvDBcL5b9bhM/93BHZQXuzWV/yjuoO6bHqIEF56Ey5t3x95hDvjYSegvJ2iwMmmwsc511RFQ67gTJrqJDZ9r+BUtNhRV1nN9RKDmcOijzAZIEzuxGh06hC9vKHI01fl5Wv98Ue7FxZNlzefpCVpI3pPAEgUauSJrCzye6zhoS/CUQICRT97TtCCCGEkH7wo1geywaB9dZrG4/jqa+Pojg9AW8uPkMqOe5p/Zbccb8BY/o4FZQKDm4PD4PViZzkyKZkezw8FAqux0z5xKIUAMD++i643B6olIEBN8/zWHMwstJ1QFiPptOqYLa7YLA6gwbc3Q43/rerDgBww7SioMdhPeWHG72VBrXBgvIQgSbLuo7M1fv0cevF3nNjt3f3t9nugoIDEjQq6cZIfkq8z0T4UF65YRLqO7vRLd408f99b5T14idGUX7OfmdsPVgs/eSAd095q8kOh1hxkNrLQW8AfMrXR+bK1tnFWLrODMnSSVPc81PiqZ+ckBhQppwQQgghA15jV7eUgTREETSHUtVmwXPflAMAqtut2FdnkL5niHCdmc3pRkOXEIywQITjOKmEvSPCXeUVLSZMeHgNnv+mPGxPOQCUZuiQFKeCzenB0ebgZfa1Hd2obLNApeBwzvCsiM6BBcihPtvP9zbAZHOhKC0BZw8N3medpQ9cuxWsfD1UpnxKSRr+ee1EvHjdxKDnxl5ntDkx5+/f4fIXN8Pt4SOevM7kJsdjSklayKF8bCXbnAg/O4ZNn69nQXkM/eQAkK4TjsOuLZWCQ2KUK9CCYWvRAGBUrncmQHaMQ94YNk8BELLk1E9OSPQoKCeEEELIgLet0lsSHW41VqQe/OyglIUEgK8Pend/R5opr+mwgueFoVnpskwmC/aCDRALZlN5G4w2F1btb+wxU65QcD2WsG8+JvRDTyxKiTgwlILyIOfcZrbj72uOAgCuO7MoZKkzy5TLGazOgM8z1Eo0QNjrXZrpO0Hdf/r69soOtJrsONpswuZjbd5+8igztPKd8rtqOvHPb8rRbrbjx2NCRcb5o6ILytnv3eEWrqtYM+VDMnXITNKCbZNLSVD3SaCbK/aUKxWcNKANiH0dGiM/1qyhVLpOSCxiCsp37dqF/fv3S19/+umnuOyyy/CnP/0JDkdsg00IIYQQQkKRB+WRZrJDOdRgxMayVqgUHIZnC2W864+0St+PNCiX+skzE32CJhacdUQYlLPS38o2i9RTHq6HeHyhkOU8UN8V9PubxaByeg8D3uRYgNrV7cTyLdW47MUf0Ga2w+Phcdf7e9BismNIlg43TS8OeYxQa7X8S9hDZcpDkaavs6C8yjvk7uPd9dgr3pyItmxaCqJdHtz9/h78Y20Zbn5jGxxuD0rSE6Ry+kj530iJNVOuUHA4V5alD9ZOEIth2UlQKjgMydQhTq2UBspl9bDyrSdsVzkAzBhMQ94IiUVMQfmSJUtQVlYGADh+/DiuueYaJCQkYOXKlbjnnnv69AQJIYQQQnyC8u7eJQCWb6kCAMwfk4NzxDVW3bKhbJFmuEOt4UpN9GZgI8F6kO0uD5xuIT0abgXWUDEIYv3scjzPS0F5NAGSPChfsaUae2oNWH+kBWsONeH78jbEqRV46fpJSAgzTTzLL+OqFQfM+ZewRxuU+5ev76jyXguf7mnAj8fboVJw0uC2SCVolNJatKp24RwP1Asr3+aOzI46O53m1/edFGOmHADOHSkLyvtgyBsgrD37/I5ZePu2MwAAmTrh95XTy6B8XEEy5o/Oxi/OLkW6rndZd0JOVzEF5WVlZZgwYQIAYOXKlTj77LPxzjvv4M0338T//ve/vjw/QgghhJzm2s12lMv2XfcmU95ldeLj3fUAgJunl2B4TlLgcyLNlItBcUm6X1DOepUj7ClnQTmjUSoQH2ZgGbsJwG4KyJW3mNFmtkOrUkhD4SLBetgNVqc0SK+q3YL9Yjb+8okFGJYd+FnJxamV0MsC0aklaQCEnn25UCvRQp+bNyi3Od3YVyecU0qCWupfv/3sUgzJii6zzXFcyJsf543seUCeP/+MdqyZckDozWY3DHq7o1xuVJ53iN6EohQoFZxUeRErtVKBV2+cgj8tHNkXp0jIaSmmoJzneXg8Qr/MunXrsHDhQgBAYWEh2tra+u7sCCGEEHLaY9O82QA1k80Fl9sT7iUhrdxZC5vTgxE5SZhakho0KDeIwd9Hu+rCDmurbA++G5tNyo50V7n/CrbkHnqI2UqvFpMdZrvL53ubK4T/DptakgatKvLhYCzwazPb0S6+58o2C461CO9xaIQBLyuFjlMrMEm8KSDPlLs9PEziOUdcvi4+z2hzYV9dFxxuDzKTtLhpegkAoDg9Ab87b2hEx/In3/99RkkaUhLUKEpLwJSS1KiPxSbvMzpt7MF0olaFaWKlQ1+Vr/v7+5XjseP+uT7l54SQ/hFTUD5lyhT89a9/xfLly7FhwwZceOGFAIDKykpkZ0d/Z5EQQgghJBS2O7tYlpE22lyhnh4Sz/N4b3stAOCGacXgOA5DsnQ+gRQgZGQ/2FGLuz/Yi+fFCe3BsEx1YKZcCMaaumz4+mATLPbQ52qxuwLK3HsqV06OVyNDnNBdJcuWt5hsWL6lGgAwPcreXvYzj7V6KxKOt1qkr/1vPITChr0VpCagSPxc5D3lbMp9nFoRdabc2O3EdrF0fWpJKpacXYrfnjcUr988NaJVaMHIs9Dzx+Rg/e/PwRe/nQV1kFVzPZFP3gd6V74OADdPL4ZGqcBZJ2h4mkLB9cmqNUJI78UUlD/77LPYtWsX7rjjDtx///0YMmQIAODDDz/EjBkz+vQECSGEEHJ6M8syq6wkOJpd4szuWgMqWsyIUytwyYQ8AIBWpZSmdkvHtjqlfm02hM1fXacVrSY7lAouMFMuZja/OtCEJct34qXvKkKeU7DjR1KuzG4EsMnjnRYHrn51C461WpCZpMWiSQU9HkOOBb5lzd6gvKrdgiqxGiDSoWcsKC9MjUdxegIAoLzFhG6H0LO/cqew63zhmNygO9bDnVuXT1CehkStCnefPyzqsnU5eaZ8YlEKUhM10mC5WMiz2r0Nys8bmY0jj1yASyfk9+o4hJCBL6a/LcaNG+czfZ156qmnoFT2fo8iIYQQQgjDMs2JWiWS49Uw210wWB1YfcCEgtR4jMmPrCd25Q4hS75wbK5P4DU8JwnlLWaML0zGDxXtMHQ70SjuiO4K0b/+3VFhWvukohQk+QVxqX7lxvIhdf5Y6bqCA9h671A7yuUGZSRiR3Wn1Nf+xf5GVLZZkJcch3dun4ac5OiGd7EbAayfHABsTqFFQKtSID8lPqLj5KcKzyvJSMSw7CTotCo0G+247t9b8M9rJ+LzPQ0AgCunRH7TgPWpm+0uadL6xKLoy8uDYUP5NEoFRufpe3882Q2V3vSUM6HWzxFCTi0x7yk3GAz497//jfvuuw8dHcI/NocOHUJLS0ufnRwhhBBCiEXMsiZqVFLwuLvGgF/+dyfueGdXRMewOlz4fG8jAOBnUwp9vnfBmBxoVQpcMVEIFN0eHsfEYDdUX/h3R4X/3jlneOAua/+A+EC9MWQPPBvyNqEwRXoskkz5IDE7zzLZLUYbACG7WhLlajAg/I2A0kxdxMHhTdNLsHTOYNx+VimS49V4Y/FU6ONU2F1jwLl/3wCT3YWC1HhMGxR5eb18n3mn1QkFB2mVXW+x9z0qTx9VD34o8hsyse4pJ4ScfmIKyvft24ehQ4fiiSeewN///ncYDAYAwEcffYT77ruvL8+PEEIIIac5b6bcG5SzMubazm5pAnc472ytgdnuQnF6As4clObzvYvG5eHgsvm4YlK+NPGa9YsbgpTJ211uae0YW6kmNzpPj/sXjsSbi6ciUaNEt9ONClmvtpw3KE9FkphZjWQFFiu5Z+XrbCCd/1quSIW7ETA4wn5yAMjWx+EP80cgT8ysTy1Jw/9+NQPDs5PgEG9MXDm5IKoMsFqpQKLGGzCXZCQiXtM3lZkjc4Xgfk6Qmyux8AnK+yBTTgg5PcQUlN99991YvHgxysvLERfnvRu8cOFCbNy4sc9OjhBCCCGEBeU6rQopYmaTrepye/gep5x3WBx4ThzY9qvZg4NONlcpFeA4DslicMoCfYPVAZ73Dfq3V3bC6nAjK0mLUbmBJc8cx+H2s0txzvAsqbSerfHyVy8G5QWp8VL2O6JMeYbQR13ZagbP81JQnq7rm6Bc/hFF2k8eytDsJHzx21n484UjceXkAiyeOSjqY8iz5SNzel9mzlw6IR8b/nAO7jh3SJ8cLyWx7wa9EUJOHzEF5du3b8eSJUsCHs/Pz0dTU1OvT4oQQgghhDHbhfL1BK1SCprlu73lfdDBPLO2DCabC6Ny9bjKr3Tdn/9EcKebh1Usn2e+OiCUwc8elhl2dRkAjCtgQbkh6PdZT3lBarxUws4C7nCK0xPAccIU+g6LQ1pjFnOm3K98fYQs8B3ci0FqjFqpwM/PKsXfrxof8dR1OflrWHa7rxSnJwZM4I9Vmk+mvO/2ixNCTm0xBeVarRZGozHg8bKyMmRmBpZxEUIIIYTEyuqQZ8oDA51wQbnN6cZ722sAAA9cNKrH4CvY8eWZ+DUHm7Biq3C8heNyezz3cQUpAEJnyuukTHkC7l0wAh/+cjoWjMnp8bhxaiXykoUS8eNtll6Xr8epFdCovP9ZKC/xj6Z8/USRZ8pH9GGmvK9RTzkhJBYxBeWXXHIJHn74YTidQp8Vx3GoqanBH//4RyxatKhPT5AQQgghpze2Ek0+6E0uXFBe19kNp5tHokaJaaVpIZ/HBDu+QZzAXtFixl3v7wEg7JCOpA95vBiUH240wu7yzbibbE4pw52fGo8EjQpTStIi7rcuShNWjtV3dnvL1xO1Eb3WH8dxPjck2J5zpYLDoBgGx/U1+bT8kX0wJf1ESenj6euEkNNDTEH5P/7xD5jNZmRlZaG7uxuzZ8/GkCFDkJSUhEcffbSvz5EQQgghPxGz3YVP99RLgfBA4DPoLciU8JYwQXmtWB5emJbQY6k54JuRZQxWJ1xuD+7+YA8sDjemlabhzxeNiujcC9PikZKghtPN4+uDzT7f21ljAACUpCfEVNKdK055rzd0S9n81MTYS6bl5zAmPxl/uXgUHr9iLBI0/R9csnPTx6mQF+W6t5+SvFKBgnJCSKRi+tsiOTkZa9euxaZNm7Bv3z6YzWZMmjQJc+fO7evzI4QQQshP6JXvjuGF9RX4/fnD8Jvzhvb36QCA1NOdqFUiWFwdLlNe2yEE5Syr3JNgQb+h24GXvzuGfXVd0Mep8OzVE6FWRpbX4DgOiyYV4PVNlfi/lXuRnaTFpEIh07u1shMAcGYU68HkclOE4PRQoxFsFp3/jvRoyLO8mTptTAPZThQWlI/I1Ud0c6W/ZCYJlQr6OFWf9akTQk59vbqFN2vWLMyaNauvzoUQQggh/eyHY20AgIau7h6e+dMxyzLlwYLhVnPooLym3Zspj0SwjHV9Zzf++W0FAGDZpaMD9pD35L4FI1DdbsW6w81Y8t+dWP2bGQCAbeJatzMjKKsPJlfsKT8oTqJPjldHfLMgGLazOzVB7dNfPhAUpgnvdVJRaj+fSXjF6Yn4/fnDIr7eCCEEiDEof/jhh8N+/8EHH4zpZAghhBDSf2xONw6IAZ7RNvDK13VaFTyawJ3krSZbyNfWRJspl2WLEzVKWBxu7Kk1wOH2ID1Rg8sm5Edz6gCEdWsvXDcRl7+0GYcbjXjky6M4OwE42GACAJxZGmOmXLw5UCXeeEiPccgbw957VtLAKw+/9owiFKYmYNrg2D6rn9JAqTAhhJw8YgrKP/74Y5+vnU4nKisroVKpMHjwYArKCSGEkJPQ3loDnG4h6DUNqKBcXImmUUKl8GZw81PiUW/oDl++Lk43jyUoH56ThF01BuytNQBga8hiK0mOUyvx1JXjcOmLP+DLA01oz1LA7eFRkBqP/JT4mI7JMuVMrJPXGTbojZVgDyRxaiXmjsru79MghJATIqagfPfu3QGPGY1G3HLLLbj88st7fVKEEEII+entqO6U/mzsdvbjmXg5XB443B4AQqY8Tq2UvjexKAX1hm6fQW+rDzQhKU6FmUMywPO81FMeaTmxz+qtXD121RjQ0CVk4ovTezeFfEx+Mm4/qxSvbDiGLS3CzYVY+8kBIC/FN6Pd26A8XScE47kDeJAaIYScivqsYUiv12PZsmV44IEH+uqQhBBCCPkJbRd7nAFhXddAwHaUA0JPeZxaCa3Y7zy5WOgvNtlcsDndaDXZ8esVO/GLt3fA4+HRaXVK/egFqZFlo1N89mEn+Xwv0mx7OL+fNwy3zyoBB6EiYUYvyrGT49WIl92kSNf1LihfNCkfN00vxu1nl/bqOIQQQqLTp7saurq60NXV1ZeHJIQQQshPwOPhsVOWKR8o5essqNaoFNIQs/REDRq6bBiTnwyNSgGHy4NWkx0Nhm54eMDicKPNYkejQchwZ+u1Phn2cApSE6BScChOT0CmzreMuy+CcrVSgXvmD0OioQLqvJG4eHxezMfiOA65yXE43mYB0PtMeZY+Dg9fOqZXxyCEEBK9mILy559/3udrnufR2NiI5cuXY8GCBX1yYoQQQgiJHM/zvVoVVd5i9gnEjQMmUy6uQ9N4g+o/LhiB3TUGTCpKRaZOK/SVm+3SUDcAaO6yRz3kDRD6qT+7YxZSE9WoarP6fK84ve8mag9KAhaeNQjqXk45z02RB+UDrxecEEJIz2IKyp955hmfrxUKBTIzM3HzzTfjvvvu65MTI4QQQkhkLHYXLnz+e0wqSsXTV0+I6RhlzcIk8MGZiTjWaoHN6YHT7enViq2+IF+Hxlw6IR+XilPQs/RaadhbrSwob+zqloLywtTogulRecIecYPV98ZEUR8G5X1FPuytt9PXCSGE9I+YgvLKysq+Pg9CCCGExOhAfReq2q1hp5D35HirkG0dX5iCY+KfTTZXr0uie0u+Di0YVmLeavLLlBttqOuMbsibP/kk9gSNMqCcfSDIkw1l6+/fFSGEkNj07+1vQgghhPRak1HonbY43LA53TEd43ibGQAwNCsJCWKpeF9OYN9W2YFz//4dvjvaEtXrLEEy5XJsfVeLX1De2GWTys9j7QVPTfAGuUVpsa9DO5FyZJlyCsoJIeTkFHGm/Iorroj4oB999FFMJ0MIIYSQ6DWKK7sAoNPqCNhfHYlKsS+5NDMR+jg1rA53nw57W7W/EcfbLPh8byPOGZ4V8evkO8qDydYLmeLaDitqOrqlx5uMNhxrFW40DM7SxXTObNK73eXpkyFvJ0KubC1ab6evE0II6R8RB+XJyckn8jwIIYQQEqMmWVDebo4+KOd5XipfL81IRFKcCk3G3q1Fc7g80MiGmNUbusX/bw31kqAsjvDl6xMKUwAA35e3oc3sLd+vaDFL+8tLM2PfL56aoEGT0danQ976Uh5lygkh5KQXcVD+xhtvnMjzIIQQQkiMGgzeDHGn1RH161tNdpjtLig4YZhZUpzwnwexTmB/b1sN7vt4P/590xScNzLb5xzrOrvDvTRAsEFvclNKUqFScD4BOQDsrxdWtGYlaaGPUwd7aURSEtRoMtpQlB57YH8ilWQkYHBmIrL1cdCqIlv7RgghZGAZUD3lf/vb38BxHO68807pMZvNhqVLlyI9PR06nQ6LFi1Cc3Nz/50kIYQQMsCwnnIA6LBEH5SzlVqFaQnQqpTQxwtBrDHG8vXvK9rA88AHO2qlx1hQ3tRlg9vDR3wsqz1wJZpcgkaF8WK2HAD04g0FXvwRgzNjK11nxhekQMEBU0tSe3WcE0WrUmLNXbOx4udn9vepEEIIiVFM09cB4MMPP8QHH3yAmpoaOBy+/wGwa9euqI+3fft2vPrqqxg3bpzP43fddRe+/PJLrFy5EsnJybjjjjtwxRVX4Icffoj11AkhhJBTSqNf+Xq0WOn6oAwhG5wkZpZj7SlvE8vGfzzWDreHh93lRqe4Xszl4dFstCEvJbIS+54y5QAwrTQNO6s7AQBTStLw7RHvMLnelK4DwONXjMW9C0YgdQCXhisVA28AHSGEkMjFlCl//vnnsXjxYmRnZ2P37t0444wzkJ6ejuPHj2PBggVRH89sNuP666/Hv/71L6Smeu9Ed3V14fXXX8fTTz+Nc889F5MnT8Ybb7yBzZs3Y8uWLbGcOiGEEHJKcbg8PqXbsZSvV4qT10szhKyyVL4e4/R1dj5GmwsH6rvQYLD5fL/eEHkJe0/T1wFgWmm69Odh2Uk+q8x6mylXKLgBHZATQgg5+cWUKX/ppZfw2muv4dprr8Wbb76Je+65B6WlpXjwwQfR0dER9fGWLl2KCy+8EHPnzsVf//pX6fGdO3fC6XRi7ty50mMjRoxAUVERfvzxR0ybNi3gWHa7HXa79z9OjEYjAMDpdMLp7LvVLn2NndtAPkdC6DolA93peI3Wd3ZLpdoA0GqyRf3+K1pMAICitDg4nU7oNMI9+y6rPabPUn6T4PuyFozO0/t8v7rNjAn5SREdiw2bi1dxIc9lXJ4OaiUHp5tHfrIW2UlaGMTMfIn4ngaS0/E6JScXukbJQHcyXKPRnFtMQXlNTQ1mzJgBAIiPj4fJJPxjfuONN2LatGl44YUXIj7We++9h127dmH79u0B32tqaoJGo0FKSorP49nZ2Whqagp6vMcffxzLli0LeHzNmjVISBiYk1Pl1q5d29+nQEiP6DolA93pdI0eMwLyf84PH6vBqlVVUR3jQLUSAIfWYwewqm0/Guo5AEocrqjCqlXHozqWywN0dXvP57NtR1GXzgPw9oSv37oH6vrdER2vul4BQIGKIwexquNAyOcN1ytwsJODqWofFHbhNQBQfWAbTOVRvYWfzOl0nZKTE12jZKAbyNeo1Rr5tpGYgvKcnBx0dHSguLgYRUVF2LJlC8aPH4/KykrwfOTDW2pra/G73/0Oa9euRVxcXM8viMB9992Hu+++W/raaDSisLAQ8+bNg16vD/PK/uV0OrF27Vqcf/75UKtjnxJLyIlE1ykZ6E7UNVrZZsHxVgvOGxn5fu2fyhf7GoGD+6WvNfp0LFw4Napj/HHHOgAeXDF/NorSEtC5rRZf1BxGXGoW/tvoRpZOi8cuH4UETc//2dDYZQO2bpS+rrKocPbYIuB4lfSYLrsICxeOjujc3q7fBhgMmD51Ei4YnR3yeWed60K7xY6S9ERUfXIQh3fWI06twHWXLoBigPVc09+lZKCja5QMdCfDNcoqtiMRU1B+7rnn4rPPPsPEiROxePFi3HXXXfjwww+xY8cOXHHFFREfZ+fOnWhpacGkSZOkx9xuNzZu3IgXXngBX3/9NRwOBwwGg0+2vLm5GTk5OUGPqdVqodVqAx5Xq9UD9hcmd7KcJzm90XVKBrq+vEY9Hh4/X74bNR1WfPGbWRiTn9wnx+0rrRahPC4rSYsWkx2dVmdU793mdMPm9AjHSE6AWq1GaqLw7+jWyg7pey1mO16/ZWqP68W6bEJmIDNJCyXHoclowxf7hOq2kvQEVLVb0dBlj/gcLQ5h+npygjbsa9LUaqQlCcPj8lKFyrjSDB202oHbD05/l5KBjq5RMtAN5Gs0mvOKKSh/7bXX4PEI/0izdWWbN2/GJZdcgiVLlkR8nPPOOw/79+/3eWzx4sUYMWIE/vjHP6KwsBBqtRrffPMNFi1aBAA4evQoampqMH369FhOnRBCCInK9qoO1HQIgWZlmyXmoLzb4UaH1YG85DhwXN9lbtkQtdF5erQcbUVnlCvR2GA4lYKDThymxga9sYAcALZXdeLl747hjxeMCHs81k+eodNiSnEqlm+plla2TS1JQ1W7NapBb1YxKA836M3fiByhMm5iUUrEryGEEEL6S0xBuUKhgELhHdx+zTXX4Jprron6OElJSRgzZozPY4mJiUhPT5cev+2223D33XcjLS0Ner0ev/nNbzB9+vSgQ94IIYSQvvbJngbpzy0me5hnhsbzPK779xbsrjEgLzkOvzxnMG6aXtIn59ckrkMblafH+qOt6LQ64PHwEZdsd4qZ9pQEjXSzwD8bnqHTos1sR3W7pcfjtUpBuQYXjMnB8i3V0vemDkrDyp11aDB0g+f5iG5OeKevB99THsy8Udn4+NczMDwnsmFyhBBCSH+KaSXakCFD8NBDD6GsrKyvzyfAM888g4suugiLFi3C2WefjZycHHz00Ucn/OcSQgghdpcbX+7zBuWtMQblhxtN2F1jAAA0dNnw2KrDsDndvT6/ekM3ypqFYaujcoUMvocHDFGsMjOImXL5GrEkv6D8zNI0AIDZ3vM5s0x5pk6LMwal+Rx3SnEqOE7IwLdHkNHneV7ala6LIlOuUHCYWJQaUQ88IYQQ0t9iCsqXLl2KL7/8EiNHjsTUqVPx3HPPhZyGHq3vvvsOzz77rPR1XFwcXnzxRXR0dMBiseCjjz4K2U9OCCHk5HO0yYQbX9+KXTWd/X0qAdYfaYVRDAoBoMVkC/Ps0L4QA/vzRmQhLzkONqcHm4+19ercPt/bgFlPfIvjbUL2eli2Dnqx7LwjihL2TnF1WKpPUO4bzE4bJATlLGsdTptJ+NkZSVqolQrMHSkMZ+M4oDAtAdlJwmDXus7gJewGqwO/+u9OfHO4GV3dTjjcQgl9hi5wXgwhhBByKogpKL/rrruwfft2HD58GAsXLsSLL74oTTh/++23+/ocCSGE9BGjzYkD9V39fRo+Vu6oxfflbfjvj9U9PzkCPA88900FPt/b0POTg3ht4zH8/oO9cHt4/CgGzhk6YVhYLJlynueFCekALpuYj7mjhCB17aGWmM6PWXe4GTwPjMnX45UbJmNodhLSEoXzjCYoN3SzTLl3IJo+3hugZ+i0GJShAwCYbaGD8laTHQ2GbllPuXC8C0YLN9LzkuOhVipQlCYMYQtVCr/mYDO+OtCEl787hmajXTw3NeLUkZevE0IIISeTmIJyZtiwYVi2bBnKysrw/fffo7W1FYsXL+6rcyOEENIHNpS14miTUOL8h5V7cdE/N2FvraF/T0qmtlMcohZBv3IkqszAC98dx0OfHYz6tTzP4+m1Zfjfrjrsr+9CVbtwbmeWpgMAWozRB+X767tQ02FFvFqJ80Zm4Twxc/zN4WZ4PJGvEfVXJWbI75gzFBeMEQJfb1Ae+XkagmTKEzVKsJb0Mfl6qZ/bHCJTzvM8Fr28GQue+x7lLWYA3sz2uSOycO+CEXjsirEAgNLMRADAsdbgv292PTQYutEsDojL0ffN2lRCCCFkIOpVUA4A27Ztw5133onLL78cZWVluOqqq/rivAghhPSBekM3bv7PNvxi+Q4AQIUYMFX1UQDcF2o7hDJmFmT2VpVJiCbbLQ443Z4enu2r0+qUJo5XtpmlqetTi1MBxFa+/vVBob3r3JFZSNCoMK00DYkaJVpMdhxoiL1qgd0wKMlIkB5LE1eZdVgi7yln09pTZZlyjvNOYh+dp5f+bHEED8o7LA7UdFjR1e3E4UZhLysLyhUKDr+cPRizh2UC8Ablx1vNQY9VL5a1N4uZdwDIoqCcEELIKSymoLysrAx/+ctfMGzYMMycOROHDx/GE088gebmZrz33nt9fY6EEEJiVCsGlXWdwrTrLnEAmCWCgV0/FZYZ7bQ60WWNPJgMpdrsnegd7XqwBtmqrvJms/T5TRV7qjutTjhc0QX6LHieXCQE9lqVErOHCwHqN4djK2HvtDik32VxWqL0eFqikO2OJlPOesqTE3yHu7ES9jF5ydI6slA95Y1dgTcrQvWAl4ql8MdDZMrrxN+B28Njv9hqkZ1E/eSEEEJOXTEF5SNGjMDq1auxdOlS1NXV4euvv8ZNN90EnU7X1+dHCCGkF1hQ6vbwMHa7pFLlSAZ2/RS6up3SdG2gb0rYWaYcANrMsQflP1S0weXhoVEpMCJHD7VSOC5b+RUp1oeeKQssp4nl8JH093dYHAFl7qzSIUcfh3iNt9eaZcojmWzOsOnr8kw5AFx7RhGmlqRi1tAM6MTBb043D7sr8IZO0KA8SRPwGODNlFe2WYKW79fLBsCxifXZlCknhBByCospKD969Ci2bt2K3/3ud8jOzg75vHfffRcWy8ApkSSEkNNNh9UbnNV2WuESgyDTAAnKWSaaqWwLXtIcqRaTHZ0Ob1DeHkXGGPANyvfWCQFzUVoClAoOmWLmt8UYXQm7tCJMFpQPzxb2Zx8Re/1D2V/XhSl/XYv7P9nv83i1mH0vTk/weTxd7CnvqUKgqcuGuU9vwL+/P45OKSj3zZQvnTMEK385A0lxaiTKVosFG/bW2OU7SZ3jgLSE4EF5YVoCVAoO3U43mvw+S5fb4/PYUXHdW7aeMuWEEEJOXTEF5UOHDo3oeUuWLEFzc3MsP4IQQkgf6JBliitlPdsDJVNe1+kflFtDPDMye2t9M89tUWa1g2V8S8TAN1PM1rZEOYGdZcrl5dwjcvQAhJ5/ky10yf6hxi54eOCLvY0+/fHsdzkoI9Hn+aliUN5TpnxjWSsqWsx4d1uNtNM8JUQQDQBKBYd4cfp5sNaHBoPv55aWoIFKGfw/MdRKBYrEz/SYX195k9EGtyx7zv5MPeWEEEJOZb0e9BYOz8c+VZYQQkjvyTPlAzEoZ0PemN4Oe9tTZ/D5uj3K8vV6Q+Du7CKxZztLzHRHsxbN5nRL5fnyTHlyglqaKF7WbAr576WxW3itye7CrmrvHne2Tqw43TcoT49wJRq7GVLTYUWb+H78y9f9sb7yYBPYm8RMeWFaPADf9xrM4MzgfeX1IXaX0/R1Qgghp7ITGpQTQgjpX/IyZnlQHmq11U+NDXkbLPYZ93Yq/M5qAwAgOV4IIKPtKQ+aKRenm7OgPFymnOd5nz5plqnXKBXQx6l8njs8J0k8506c/8xG3Pbm9oDjGWVZ9A1lrdKfpcnrfuXraRGWr9eKwa/TzcMo3jRI8Stf96cT16IFm8DeIH5uv5o9BBeOzcWvzhkc9lihJrAHuykCUE85IYSQUxsF5YQQcgqTlzEfH4CZ8joxODxrqDCNvLLNEnOV1ad76rGzxgAOPOaNEuadtEdZvs56yuWl5kVpYvm6lCkP3VP+6sbjGHL/KmkPvHzIG8dxPs8dIQblr244jooWM7450gKrX8Br7PYG5d8dlQflwu+yxK98PU1Wvh7uc/Tv5Qd6DsrDZcpZT/nQbB1evH4SLp2QH/ZYg9kEdr/KCHY9ZOjk69l8vyaEEEJONRSUE0LIKaxTXr4uy0oOmEy5GBzOHJIBjgNMNldUk8Plx/nzxwcAAPMKeEwsTAYQ3RRyl9uDZnHI2Mwh6dLjJemsfF3sKTeGDvQ/3dMADw98e0RYdcYy9RlByrmHicPe5OdY4xcsG2VD1Q41GtFitMFgdUhT9P0HvbGg3O7ywOoIvfauzq9MPEGjhFalDPFsAdtV7j/ozePh0dwlfCa5yZFltFmmfFNFG6b8dR0+39sAwFu+PqU4TXpuhk4bsj+dEEIIORXQv3KEEHIK67R4M63yAM/ciz3lNqe7x57lSPA8LwWHQ7J0yEsW+pFj6St//KvDMNldmFiYjPkFHik4jWbQW7PJDg8PqJUcppYIQaFSwSE/VTivnsrXbU43ysVp4SyTLWXKg2R6Wfm6XE27b1De1e07BO7bIy04JvZhZyVpkaDxLYkXgmvhn/ZQvyO7y41mv2x/T/3kgDco96+yaLc44HB7wHGRl5mPzNUjM0kLnhd+Rx/vrgfgLV+fUpIqPZcmrxNCCDnVxRSU33zzzdi4cWOPzysuLoZaHb4cjhBCyIkTaiVYb8rXf/febsz827c+PerR6na48fm+RnQ73eA4IC8lDgVi8BuqrziU8mYTvjrQBAB45JJRUHLegWfRDHprFH9uTnIcRuYK09FL0hOgFrO0WXoWlAcvXz/caJRWzrEbC8F2lDNDsnRQKnxL2gMy5WJQPq5AyPy/u60GH++uAwBMLk6FP47jpBsSoYLy+s5u+Fe291S6DoQuX2el65k6rfRZRXKs7++Zg79fNR6At22ADaAbk58sTXvPTqJ+ckIIIae2mILyrq4uzJ07F0OHDsVjjz2G+vr6oM87cOAACgsLe3WChBBCYtPtcMPm9AT9HgvKo+3fdro9WH+0Fd1ON745HPvKy5v/sw2/fXc3AKFnW6tSIi9FCMqDDVsL56XvjoHngfmjs6XsM+sJbzPbI36P7GZAbnI8JhWl4JHLxkhBI+AtX28zO3zWdq091Iz1R1pwoN67jo0NYpN2lOsCg/I4tRJDs4Teata3Hli+LgTlPz+rFBqlAnvruvD+9loAwI3Ti4O+j56CcjbkTSW7IRBNUO6/Eo2tQ8sVf3+RilMrMV682dBg6IbHw0vHyk+JR26K8HnTOjRCCCGnupiC8k8++QT19fX41a9+hffffx8lJSVYsGABPvzwQzidofetEkII+enI16H5M9ldONpkwtRHv8Fbm6siPubxVgscLiHQ33K8I6bz4nkee8RBaIsmFeD5ayYC8PYjN0aRKT/aZMKne4Qbw3fMGSo9npYoBJl2lweWML3VcuxmQH5KPDiOw43TijGxyJuNztBpwHHC7mwW8LYYbViyfAdue2s7vtzfKD23q9uJTovDu6M8xIqwJxaNw0MXj8IvZwvTygMz5cLNk0HpibhwXC4AYWL68OwkTC9NRzA9BeUsGz1JlmkPt6OcCTV9na1Dy4uwn1yOBfJGmwvH28xwuD1QKTjkJsdJ7Qy0Do0QQsipLuae8szMTNx9993Yu3cvtm7diiFDhuDGG29EXl4e7rrrLpSXl/fleRJCCIlSuLVYDpcH3x1tQZvZjnVRZLwPNXqzwdurOnzWf0WqQ+xBBoDHrxiL8YUpALwBWkOEmfJuhxu/eXcXPDxw/qhsjBWzrgCQoFEhQSMEkZFOYG+UMuXBg0CVUiGVxbMS9u1VnfDwgIcPvElR2W5Ba5hMOQCML0zBLTMHSavNQmXKk+PVuGGaNzN+04zigGnujH9QfrzVjKXv7MK6Q8Lvme2GH5mTJPVrp0aRKTfZ/MvXhc8iJ4agXKdVSavifqhoByBUDaiUCswamgGVgsPUQYFl+oQQQsippNeD3hobG7F27VqsXbsWSqUSCxcuxP79+zFq1Cg888wzfXGOhBAyYP3tqyO4/KUfpIzoQOBye3CowSiVTif57cdmqsUA0GiLvL/8YL1R+nNXtxNHmkw9vmZHVQfWH22RvmZBXIZOC43K+89QrpgRZT3KPXl01SGUNZuRmaTF41eMDfh+ui66YW/1Yul0Xpgy7EyxhJ39vrdXBVYLsAC7ut3iLV8PkSlnCsXy9bqObqk03un2TlDXx6swqSgFl03IwxmD0nD5xNArx+Rr0ZqNNtz4+jZ8ua8Rv1qxE5uPtUm74QvTElAqribrzaA3dhOFZbajxT7vTRVtALxr3n45ezD2PzQfMwZnxHRcQggh5GQRU1DudDrxv//9DxdddBGKi4uxcuVK3HnnnWhoaMBbb72FdevW4YMPPsDDDz/c1+dLCCEDxgc7avHKhmPYXWPAs+vKon79l/saMfrB1dhY1trzk6OwfEs1Fj7/PZ5ZJ1QslWbqgj6PTfo2dUfednSoUQjK2YCyrZXtYZ/P8zx+/vYO3Pbmdmn4GQvK81J8M6ush7jR0HOmnOd5rNwhDDx76spxPnvFmfRE1lce2bA3djPA/7zk/Cew76gWgnL2eWToNJgmlpVXtlm95eshMuVMXko8VAoODtlaNnlGWqdVgeM4PHvNRHywZHrA1HW5dNnk+Vvf3I56QzfUSg5ON48ly3did3UnAKAgNQFnDBKmzI/I0Yc9P3YOQGBQzn6v7MZCtPLFoHzLceFaYivoACBeE35NGyGEEHIqiCkoz83Nxe23347i4mJs27YNO3bswC9/+Uvo9d5/1OfMmYOUlJS+Ok9CCBlQyppNeOCTA9LX722vjXoa+ZpDTbA43FixtRqAUBLNSs47LY6op5AzrF97r/j/C1LjoVZ6S53ZfC+2touVSIfi9vB44JMD+HBnHQ42CEH5BaNzAABbe+grN9pcMFidPru7WQ+yf68wy7S2WxywOcP3gRusTtjF3vbpg4P3VrNAONIJ7GwCeLhMOQvKW012mO0uHBI/jzvmDAEAnDkoXcr0HmowSpnunjLlSgUnTZ9nJexsHZpOq4pqT3eqGJRvrmjDwQYjEjVKfPW7szC1JBUmm0vKbBemxeO35w3Fxj/MkfrVwwk2fZ3neRxvNQMABmcmBn1dT9jNGHYTYlCMxyGEEEJOVjEF5c888wwaGhrw4osvYsKECUGfk5KSgsrKyt6cGyGEnBCHGoxY+Nz3+E5WUh2t5T9Ww+7y4KyhGZgzPBNuD49/rDka1TFYxnjzsXYcazVj9pPfYcqj63DRP7/H1EfX4ewn10t7r6PR5NeTnZ6o8SlPZsEwC0K7up1hJ5TvqunE8i3V+L+Ve9HV7YRKweH6aUUAgB1i1jUU+bAxVsLO3rd/73ZKghpxauGfJZYtDoVlqlMS1NCqgmdTM3RsLVrP5evdDjc6rU7xvMKVr4uZcqMNu2uEfvKC1HjcOXco3lw8FQ9fOlrK9LIserxaKQW04bBMM6tgYOvQ9CHaD0JhmXIWfE8qTsWQrCS8desZuFmc2K5VKVCcngilgkNRemQZbilTLhv01mKyw+JwQ8Eh4uP4878JMiidgnJCCCGnl5iC8htvvBFxcTQNlRBycnpt4zEcajRi5c66mI/BeomvO6MId50/DACw5lBzVCvGWPBssrlw/8f70e10w+3hcaBe2Hft9vDY2UPQG4x/QJsmC8p1WhWSxT+zGW1ONx9ydRqAgH75odlJGCXu8W4z28NmteUB8dbjHbA6XNL7zvELfjmOk7LlDT2UsLNzygqTgY6mp7xBzN7LB48FIy9f314pXANTS9LAcRzOGZ6FdJ0Wg8RMuUEM8nvKkjP+a9FYBYM+vuchbHL+/eETxUF6CRoVll06Bp/fMQsfLJkuBdmRkjLlsrL6Y2KWvFBcaxeLfL+gvCQjtuCeEEIIOVn1etAbIYScTJxuj1RG3RbjcDajzYmjYgZ7ckkqhmULu7EdLo9UctwTnufRJAue2eTuBy8ahb9fNR4Lxgjl4ZXt0ZXE+x8XEIJytoc6OV4trbbyf0+htPtNcR+Vq0dyvBpacUhbizH05yh/rcPtweaK9pCZcsA7wbunYW9s+jnbHR5MQaoQ3B2PoK2A9bHnJseFnGoOeHdmt5jsUpXA5GLf6eDF6QmQH4LdwOgJC+ZXH2yC2e6S1qHp46ILytnNCGZCUYrP12MLkqWJ99HQSeXr3pswx1uFz7Y0I/bstrwyQaNSxDwwjhBCCDlZRXebnBBCTnLbKzukaeOtEU7l9re7xgCeFzKbLChMjlejq9uJFpM9op3PnVantO+bSUvU4IZpxdCoFDDZnPjqQJM0RCtSxm5XQNY7NUEjTeROTVQHzZAau53IDrEPmmW7R+XqkZmkxeKZJeA4Dtn6ONR0WNFssoUsXfbflb3+aIt00yBYUM4CtMYe1qKx8vVwWegROcLNkkgmxLNS/tww/eSAN1PebLRJ5eUT/ALcOLUS98wfgX11Bpw/KhsLx/bcrw0Al0zIw6sbj6OixYzfvbsbc0ZkAYg+U56W6PuZjC9ICf7EKAUb9CYF5SGGCUZCPlivJD0BCkXomyKEEELIqYiCckLIgMPzPJ7/pgLDsnVYEGFAE6k1h7w7uWNdY7ZTLF2fIsuQZiVphaDcaJcy5+GwTLBGqZB2dl82IV9aEcaGhVW3W4MfIAQW8CZqlHC4PXC6eaQnaqQbBSnxmqD9zeEy5SywPndEFv5v/nDp8Wy9VgjKw/R/s9dmJWnRYrJj/ZEWdFiFx4L1brMAraGHIXcsOx+ufJ39HlpNdnRYHNKNiWBY+Xp+mMnrws8Tvl/XKTxfreSC/r5/dc7gsMcJdex/3TQFV7/6I7450gID6ymPj+6f6uR4NTgO4Hkha5/ew+T3SCWKFRaszUKp4HC8TShfL+3FcLZsfRwUnNBOMagXGXdCCCHkZEXl64SQAWd3rQHPrCvDPR/ug8cTeY92T3iex1pZUG6yuXqc8h3M9iqxbLlEFpTrWa9xz+u8AG/f97AcHcbk66FRKnDtGYXS99mwsKp2S1SfAQvKC9MSsHjmIIwTS5VTxfL1lIRQmfLQu8pZCbp/WTTLrPsPlvN5rTj5fMGYHGhVCjR02aRMPvvM5FigHu6YgPdzDpcpT9SqpD7tI03GkM8DZJnyHkqn/c95eE6Sz6713ppQmIIrJhUAAHbXCNdZtOXrSgUn9ZX7Z/F7Q34zhw17YxsH2L7zWKiVCulaKqGgnBBCyGmIgnJCTjJd3U4cjaAct794PDzu/d8+vLi+IuZjlInvz2R3RdQPHOl5PfzFIdQbuhGvVkIjrpiKNlvudHuklWNTitOkx1kGtSXC47Hy7Bx9PN6+9UysuetsDJVlXAtS46FUcLA5PWiOMNAHgGbxuNn6OPxp4Uh8dscsJGpVmFycCo4TsvuRZsrZzYAOMbD2zzRn63t+zx0W4Xv5qfE+q8vSEzWIUwf2trP1WA1BgnKHy4P/bKpEebPJO+gtRMk9M1wsYe/pfzPe3enhg/I4tRJJskFwo3OTwz4/FqPyhB50di8m2vJ1wPu76sugXKtSQCWWlpttLthdbtSKQ+liXYfGsGFvvelNJ4QQQk5WFJQTcpJZumIXLnhuIw7Ud/X3qQR1sMGI97bX4pm1ZXDHmOUubzFLf95fb+iT83ry66N444cqAMADF42SMqyRTOaWO9pkQrfTDX2cCkOzvNlBaSp3mKFnck2yYWdpiZqADKFaqUChuLe6qi3yEnaWKfffAX7eyGzsf2g+bpk5KGRPudz722sw5qGv8UNFG9rFwDrdr1eZ/Yxw5essy56WqMWc4Vne1wbpJwe8febHW80+VQ2A0I/+8BeH8OdPDkQ0fR2Q9ZU3hg/K2U74vBDnJSf/mWPyIxviFg3/wXDRrkQDgDnDM6GPU2HuyOy+Oi1wHCfd0LHYXahpt8LDC73mkU6YD2XpnCG4aFwuLhjTt+0qhBBCyMmAgnJCTiI17VZsqmgDz0PK1g40BxqEmwUuDx9xKbc/eVC+tzb4zYf1R1uwdMUudFkjm3b+0S5h/dkjl43BdWcWSUFEtJnyCvHcRuTqfQZSSfurI3zPUqY8TBDIAvWqKCaws6A8O8hxWTAeNCi3+Zavf7a3AVaHGxvKWqW+cP9MOSvlbuqy4bWNxzDn79+hzG+vOntteqLGJygPVSY+JFOHsfnJsLs8uP3tHT4VF/ViH/feOoP0PnsOyoUA90izCV/sa8DGstaA5/A8L01f7ylTLvxM72c7Or/vM+UjcpJ8prfHkim//8JR2P3gPGn3eV9h106doRsvbzgGQOgnDzexPhJzRmThhesmITmG90oIIYSc7CgoJ+Qk8tneeunP1VGuyvqpHGzwBtEsiIpEq8mO1zYeg9nuQoUssNtXZwh4rtnuwuI3tuPL/Y34aHfPu8Zdbo+UEZ8/SsgcZojDr6KdwH5c3MvsX66bFUEpt1xziIy2nNRXHkUJf3NXz8cNWr4uy5TzvLArnf3sTvHGR6ie8haTHW//WI3KNgv+/MkBn13t8oC+KD1B+tyCTV4HAJVSgQ+WTMftZw0CADz3TblUIs0y9janB1aHMAsg0vL1fXUG3PHObvz8rR0wWH0nwhusTnSLswXC3SRh2M0IBQeMzOn7THmiViX97oHoe8oZ5QmYYs6C8tve3I6Pdgl/H10+Mb/Pfw4hhBByOqGgnJCTBM/z+HRPg/R1VZRTuX8qLJgDvCXBgJBBDpdFfujzg3hs1RFc+fJmn37igw1GuMTp5G1mOzotDryxqVL6vtPtCTgWALg9PF5cX4HNFW1otzjg4YUghU2ijjVTfqw1+GCrrCiPF25XN1MirhmrjCYoN7Ge8nAD0Ly93GwAnLynvK6zW9q3fqC+S2pDSPVb9cYC/9oOqzSNfFtlB77c3whAuGbb/bLsbIjZpOKUkOcXr1HiTwtHYuaQdDhcHvztqyMAgDaTbzCdoFEGzfrLlaQnQKNSgN0ncLg9WHPQWxZvdbiwQcyeZ+iC97n7yxSvocGZOsRren5+LOQl7AMpe5wsXi8eXjjHFT8/E4tnDurnsyKEEEJObrQSjZzUDtR3YWtlB26ZUXJCskIDyZEmk09Z90DKlPM8j3pDN3L0cT5TrllQbne5ccGz30PBAZvvPS/otOrvjrQA8O6UztBpYHd5YLK5UNZshkbF4bIXN8Pucvv8rlnG1N+q/Y146uujKM1MxLNXTwAgBFPstaGC8toOK7490oKrpxYGDdCOsUx5ll+mXOopj6x8vSmK8vXjbRbwPB9RiXBTl/B+Qu0cB3zL10szddhZ3SkF4YBvtQO7QaKPUwX83tjPcPnNDnjsy8M4b0Q23Dwv7WJnWfZfzR6M+aNzehwMxnEc/nzhKFz4/Pf4cn8jltQZAvr/eypdB4TM+6hcPfbUGpCh06LNbMcX+xvxs6mF4HkeF/1zk7RrOz+C0nXA+3uZVJTawzNjNzI3Sbq5Ee1KtBPp9+cPw1cHmrBwbC6mlqT2umydEEIIIRSUk5PcQ58dxI7qTgzKSMC5I/puoFFfOtpkQqJWiYLU3vV2fn2wCQAwMlePw41G1HRY4fHwPn3N/WXljjrc8799uHh8nrTuCvCumartsEplzLWdVgzODFyfNLYgGVuOd0hfs93Pm4+1Y0NZKz7f2wCzXeh7drq9QaDJFnyV1xf7GqSfzc5Dnj0OFZQ/sfoIvtjXiASNEldNKfT5nsfDS/3dAZlyMUC1ONyw2F1BS8S95+yU3ku4oJx9ThUtZix47nv8bdG4sNO0nW6PVOId7rjyoHxQRiJ2Vnf6rESTVzswwXZdx2uU0MeppH70BWNysK+uC/Viv/GVYlY8Tq1Agkb4mQoFhyFZka3PGpmrx5zhWfjmSAt21xjQZvHNlMt7u8N57PKx2FbZjhlDMjDvmY34oaINnRYHPDwvBeQzh6RjydmR7Ra/cnIB4tRKnDM8M6Lnx4JNYAdiL18/Ec4sTceZpek9P5EQQgghEaPydXJSY8HW4R4mK/eXNrMdF7+wCde8tqXXx2KD3a6aXACVuCor0v7lE+1bMcv9+d4Gn8dZT3m1rNQ+VH90t9O3DH1olg7jxQD0idVHcKjRiLREDd5cPBV3zBki9bGagwTlZrsL648KJclON499dULmV95/nBmip5yVlQdbodXQ1Q2b0wO1kkNBqm9WVadVIUEsZWa/l4MNXTjcGBjgsn5yfZxKClaDKUxLwG/PHYIEjRJHmkx4bl1Z0Od5PDx+/tYOnPXEevA8oFZySPMrNZdL9MmUC1lfefn6gYbA4Xr+Q94YeUZ+Wmk67r9wJADg1Q3HsFecB+A/tT0aRWIJf4OhG21+13tmmBJ9uVF5etwycxCGZSdhdJ4ebg+Prw82Sb/rzCQtVvx8Gs4eFlmQHadW4srJBdJcghNhpKx8PZZBb4QQQgg5eVBQTk5aPM9LmbPy5oEZlB+o74LD5UFdZ3fM68EA4b2ywHJScSry2aqsAVLC7h/EsUCvQZxo7ROUh+iF91/JNSQ7CbfMKMGFY3OhUSmg4IAnF43DOcOz8H/zh0sZY5M9cPr6ukPNUtk0AOys7gQQWaa8U7ymgvVxs37y4vREqJSBf33KS9ibjTZc8dJmXP3qjz7nAnhXnIWaQC5397zheHPxGQCAQ0ECfAD43646rDvcLJtIHhe2gsKnfF0sxWafvzDkTfh9Jsr6pSMJyscXpmDBmBxMK02D3eXBI18cAhA4IC4aeeJn1NBlk6oA2FuLpHzd38Kxwsqt9UdbIurr7y+5yfG4bdYg3Dpz0IDqKSeEEEJI36PydXLSsjjcUrAj77UeSOTZVrPNJQ1JilZdZzc6LA6olRxG5iahOD0R1e1W1LRbMa2fS0k7LQ5pyBczb1QOXtlwDPWGbvA879P/HipT7j8Re1iWDtn6OLx4/SQYbU6YbC6fnl8WWAYrX/9iX6PP1yxjmy0rd5YPZpP3a3daQwfloSave48Zh6p2K1pMdmyt7IDd5YHdJUx+l6/aYoPFJpdE1pM8Kk8PjgOajXa0me0+GVqTzYknVh8FAFwyPg8Nhm7MH50T9nipiRooOECrUqIojWXKXXjq6yPYU2tAm9kBBQfMHp6JVfuFtomMEIE1C8rZtclxHO5fOAoXv7BJqhgIFdBHgn1uFS1mqTVicnEqtld1hu2bD2WMuMKsut2Kpi7hug03qb4/PXDRqP4+BUIIIYT8BChTTk5a7bKy44oWc68y0SeKPCiXlwdHiwWVI3P10KqU0lTu3mbKV2ytxoqt1bA5vcPS/v39cbyztSbiYxxsELK3xekJuGpyATKTtLj2DKEX22x3wdjtQnWHPFMeeM48z0t9yfcvHIkls0sxtSRN+r4+Th0whCspLnRQvvV4OwBgTL5QAsyCOXkQxwJbu8sDk9jf7fbwMIgZ45oOa8Bkd9Z/XBqkJx7wllM3ddnw3jbvZyjPxvM8j28OC9O/547MQiR0shVZ/uXw/9lUhTazHYMyEvH3q8bjw1/NwO1nl4Y9XnK8Gi9cNwkv3zBJCpg7LA68uP4YfqgQPrshWTqprx8IlykX3jO7NgFhPsD4guQeXxuJ3BThd8b2n8erlfj9vOGYNyobF43Ljfp4ReLe7poOqzTEbiBmygkhhBBy+qBMOTlptZm9mVW7y4P6zm6p/3SgONocPCjfXNEGjuMwfXBkWe69Yj/5+IIUAN7AoroXa9H21hpw/8cHAADPf1OOF66bhNKMRPz1y8PgOODCsbkRZfb3i6XOY/KT8eSV4wAIk7PTEjXosDhQb+hGjew8g2WgzXaXdFPlxunFEa2l0olBORuYxjhkQfaZg9J9hpZlycrX4zVKJGlVMNldaDXZoY9Tw9jtlFZnuTw86jq7MSjDmxVnk9dLM0JlyoXjr9xZ67PWTT41/EiTCQ1dNsSpFZgxOKPH98mMytWjss2CQw1GnDXU2/u8o1oYjnf7WaVBp9qHwsq4rQ7fz68gNR5nlKTh0on5Pje+0kL0hU8UJ5DPGe57g+G6M4uwt24/ACC9F0E5uxnDro+MJA2mlabHXCGSnxIPjhOm9rMbSrkRTl0nhBBCCDkRKFNOTlrtfgO6ygZYX7nL7fEpq2cZXavDhcVvbsfNb2yLOHu+t1YIfMeJ2UeWNe1NpnyjWEINCGXR/91SjWaj8JnyPLCv3hDRcVg/+Zi8ZHAcJ5WBs2CqttOK2k5vUN5g6Ibd5bvGjK3j0qgUEQXkgHcitcnvM5Sv9pLvegYCJ5KzvnI2QKzDr4S+ss37+7M6XFJPd6hMOet/Lmv2baeQZ8pZlnzWkIyI3ysgrMgCAjPl7CbH0OzIJpr7i1croZL1n184NhdPXz0Bs4dlSjd/gNCB9fmjsrHtT+fhzrlDfR6/eHweksQWg1ABfSQydFqf8+vN0DhAuMbY72lnlXBDgzLlhBBCCOlPFJSTk1a733qkgdJX3tjVjQc+OYANZa0+A75YUF7f2Q27ywOHy4M9NYYej9dldUqBLxtuVpLhzZTzfGxl+5sq2gAAU8W+5laTXRqkBXiz8z1hQ8HG5if7PJ4nlh3vrO6E081DreSQoFHCwwO1Hb496AarEEhHM9CK9ZT7T19nQbk+ToViv8qJ7KTgQTkbkObf187K1QHg1Q3HYbA6UZAaH/Bemcsm5uOKifkYX5CMCYUpOGuokAmXZ8rXHRYm1Ue7wo+tyDrUaMTqA034+mAT7C63tIHA/71GiuM4n+ne8gy0PCgPV4KepY8L2FedoFHhF2eXQqmIvCIkGKWC87mZ0hcTzwvThKDcIu64H6g95YQQQgg5PVBQTk5aHQFBee8z5U63B5vK23x6rKP15uYqLN9SjaXv7PJ5nGV06wzegHR3D0H5418dxsRH1sDqcEOnVUkZ2qK0RKiVHMx2V8CQtUhY7C7sqhEmkl8mrhZrNdl9gse9dYFrseQ2lrXivo/2SyX0o/N8s9L5KUJA9+MxoUe5IDXBm+H3K2Fnk7+jCcpZT7nF4faZJ8CC8uQEtc9ueI1SgRS/cnz2ffYZdlh8s+7HxfNsMHTj1Y3HAAB/WjgyZJl4ZpIWT189AZ/eMQufLJ0pBe+s1eJgQxf21Bqg4IDzIuwnZ0blCscqazbjl//diV+v2IX9dV3w8MKU9MxeBKvsc1cqOEyRDZ/LTNIiTi2811gmqN9x7hAceeSCsLvVI5Enm1KfmRR7KTwjv9kARDYFnxBCCCHkRKGgnJy0WADJ+nsrepkpd3t4/Oad3bjh9a1YsnxnzBloll21+e3dZoFnvSyIZoFxKB9sr4WHF97jgxePglIs49WoFBieI5Qzs0x1NLZVdcDp5lGQGo8pxcJAtVazHe2yPv09tYaQnwHP87jr/T14VxxmVpqZiFS/TCrL5rOe8+L0BKk/27/sngXSKdFkyuO8IzHk2fKubuE9JMerkZWkhUZcXZal1wZkc1nGtFYcRNfpX74u/i6f/6YcNqcHZ5SkYcGY8JPN5VhWl5Wvv7ReCOwvHJcX9eTwbL3WJ1vt9vDSXvji9MSA9xYNvfhZjslPRlKc93fAcRzuPn8YLpuQhxE5+lAvD4njOKiDrI6LFqu6AHpfvg4EBuVZEe47J4QQQgg5ESgoJyctFkBOE0tjK1rMMQfSAPDApwew+qCw/mlDWSs+2VMf03Gq/QJOFitJ5es+mfJOeEJMjbe73OgUy7o//NUM/GxKoc/3WRZ2fwxB+aZyoXR91pAMaTiZweqU9ooDQiDJyrr9dVgcaLc4wHHAvQtG4NUbJgc859IJ+VJ5OAAUpyVIgbr/sLeuGDLlWpVSyljLd5V7A3wNFApO2ukeLAj2z5SzHeVsun1lmwUtJhs+2iVcC/dcMDyq4FfahW62o6LFhFUHhFVtS+cMjvgYDMdxUsZZrRTO4cv9wvHY5xorVr4+rTQt4Hu/OHswnr1monRDqD/IB7GFWs0WjUK/XvloevsJIYQQQvpavwbljz/+OKZOnYqkpCRkZWXhsssuw9GjR32eY7PZsHTpUqSnp0On02HRokVobm7upzMmAwnrf2bBqdXhDpjEHanDjUa8s7UGCg6YO1Lo9X3480NSkBYpj4eXyrnZkCsWSLGJ4A2yoNxoc+F4W/AMP8uuqpUcUoNMQR+dJ7zvAw3GgO+F43J7sE4cNjZzSAaS49VSkHekyfdYofrKj4lBdUFqPH45ezCGylZnMcnxaiy7ZLT0dVF6IorF8nX/qfGxBOWAN8MrX4vm359eIAXlgdlQ9r26TpYpF147qVgo4W4y2vDAJwfgcHswuTgVU0oCg9ZwWKa8zWzHGz9UgeeFwWixZJ0BYNklo/HUleNw1/nDxOMK1yf7XGN1/qhsZOi0uFxsZRho5Dve0/ugp1yeKc9NoX5yQgghhPSvfg3KN2zYgKVLl2LLli1Yu3YtnE4n5s2bB4vFm0W766678Pnnn2PlypXYsGEDGhoacMUVV/TjWZOBgmXK81PikagRMl3yNWnR+L5cmEQ+e1gmXr5hEgZlJKLT6sQPx9qiOk6T0Qa7ywOVgsMnd8zEKzdMxrniqijWU87K11nCdVeIvvIWMSjPSgocogUIpcaAUL4eTYXAR7vqUd1uRWqCGucMz4RCwUnB4xFxr3qC+HmG6is/3ioEsYNDTCFnFozJwUXjcsFxQhaWBcHyGxMApN3g+iiDcmnYm11evu7tKQe82fCspMDgi2VM6w3dcHt46SbMoPREnD1MWDv29UHhBsbtZ4Xf/R2MlCk32aX5AYsmxR74FqYl4KophZhclOrz+KBeBuU3TS/Bjj/PjflmwYmW18eD3uRBeY6e+skJIYQQ0r/6NShfvXo1brnlFowePRrjx4/Hm2++iZqaGuzcuRMA0NXVhddffx1PP/00zj33XEyePBlvvPEGNm/ejC1btvTnqZMBgE1fT9dpkCEGP/5r0iK1qUIYRjZraCbUSgWGi5nfaDPlrFe6IDUegzN1uGBMjjSQzOhXvn6GmHXdHaKvvEUsHQ/V7zoiJwlKBYcOiwONXcHLzP3ZnG48s64MAPDrc4ZI/cMseGTD89jUcHZuT685ivs/3i8F/2wAWk9BOcdxeP6aidjz4DyMzktGQYo3CJbfSIg1U54UZC2a/7GumVqIWUMycOXkgoDX5+jjoFJwcLp5tJhsUk95aqIGr9wwCbPFwLwkPQHnj4puWjoAafiayeaSBhGygW29MTo/GfL7NLFOXj9Z5PVx+Xpaoka6kUfr0AghhBDS31Q9P+Wn09UlZOXS0oRgZefOnXA6nZg7d670nBEjRqCoqAg//vgjpk2bFnAMu90Ou90bmBmNQjmu0+mE0xnZTuj+wM5tIJ9jX/i+og36ODXGF/QuMPF4eCmATNYqkJagRnU70NxlhdMZWEodjt3lwbZKISifVpIMp9OJ5HjhP9hbTbaofifHxcCrKC1eel2COL26y+qA1WZHsxhsLxidha2VHdhdYwj6MxoNQjY6I1ET9PtKAEMzE3Gk2Yy9NR3ITOx5mvdL3x5DY5cNOXotrpmSJx03PdE3GD5veCa+PtiMvbUG1LSZ8Py3FQCAW6cJWd4K8X2WyN5nOAkq4dpOT1CC44TPvMlgkbKeBrEVIUmriOrzTtQopNez13WafY81KicRb9w8CUDw/33lJsehtrMblS0mdIjnodcqoOZ4vHTteHy8pwGTilLgcbvgiXIof7xKWAXndPNwunkkapXI1ql6/b9zrUIY/ndMHESXnxz8GjlVZCV6/6lKiVOGfa+R/l1amBqPI81mZOnUp/RnRwam0+XffHLyomuUDHQnwzUazbkNmKDc4/HgzjvvxMyZMzFmzBgAQFNTEzQaDVJSUnyem52djaampqDHefzxx7Fs2bKAx9esWYOEhIGfTVq7dm2vj+HmAQ5AP85lCqrLATy0Uwm1Anh4ihtxvZitZHECbo9w+W7d+C1cZgUABb7bsguuquiGvZV3cbA5ldCreZTv+B4VHNDeIBxvz6FyrOo+GvK1jVZgY5MC8/M9SNEC66uF18HUilWrVgEAyjo4AErUNrXhvU9Xw8OroOR48A0HAKhQ1mzEp1+sgtqvbuXHGuFYto4m6Vj+9B7hOZ9s2AVHpSfoc5iDnRz+dUQBgMPcLCu+Xfu19L3uTvG8RZaqPYhXKtHt9OChd76TvvfVt9+jUAccqm0HwKHl2D6sat0X9ucGnLNKiS4nh5WrvkGxmGg/Viv8/KqyQ1jVeTDiY1kMwut+3LEHyrrdAIBy8XOrKT+CVcbDPR4jzi08f9WGLahtET6fsv27wdcI11ESgPIWoDyaNymTqFTC4Bb+x5ilcWH16q9iPJKvVF44b42Cx47vv0Uvhq+fFC4oUMDDA5u/i+zvyJ7+Lk0W/7djqjuKVauO9MEZEhK9vvg3n5ATia5RMtAN5GvUarX2/CTRgAnKly5digMHDmDTpk29Os59992Hu+++W/raaDSisLAQ8+bNg14/MPslAeFOytq1a3H++edDrY6uhFfO4fLgwhc2IyVBjQ9uP6NXa5L62o7qTnh2bofdA3AFE7BwYl7Mx6poMQM7NkMfp8IlF83Dtk8PYf+OOuQOGoaFUU62fnpdOYBKzBmVhwsvHAsAaNhUhXUNZUjJysfChWNDvvb+Tw5ic3M9Jo0cguvOG4Iv390DNLTg7EkjsXB6MQAgvbID/z66A8o4HYZNHAns3oGC1ERcf9lMPH/kO3RanRg0YSbG+VUPbPz4AFDfgCljhmHhOcH7mdu31GDbl0dgisvAwoVTQp5nh8WB+5/ZBB4uXDu1AH+5ZJTP94+sK8eWlkrp66suvgDrTbuxsbwdW9vUAIQU8fBxk2Eo34kOu3BdXX/ReVEP3nqzbit213ahZNQkab3Yv6q3AF1GnDVtCs4dnhnxsb6zHcD+zgaUDB2BhWcNAgC8Vb8N6DRg5hmTcMHonkvONzkOonxnPdKKhsHZWAPAiflzZmFETnQVF6H8u2YLDPVCxc70kYVYuHBUD6+ITMuP1dix6ihKM5Nw4YUz+uSYA9nCCJ8X6d+lZ9tdKGs2Y2Jh8oD6e5KcHvrq33xCThS6RslAdzJco6xiOxIDIii/44478MUXX2Djxo0oKPD2febk5MDhcMBgMPhky5ubm5GTE3xXsFarhVYbGCSo1eoB+wuT6+151ndZUNVuBdoBJ69AomZA/IoBAC1mbwnH5/ub8LMzimM+VpddyApn6LRQq9XIEtddGbpdUX9+WyuFvumzhmVJr00Xh4J12cIfr9kklNDXGmxQq9Wo6RD6xQdn6aXXpeqEY5ntLjSbhM+gIC0eGo0GYwtSsLGsFYebLZg8KMPn2G3i55WbmhDyHM4engV8eQQ7qg1w8QrEa4KXHxxtMcBsd6EgNR7LLh0Ltco3LZ+T7K0iSYpTQRevxeTidGwsb4fF4a3Z7rJ70GoDeAg929kp0e/Hzk9NwO7aLrSYndL7Yv326bq4qH5/yfFCf7HV6Qk8VlJkxypKE4ak1Rvs6OoWXpuVHPozj5YwYE74S3l0fkqfHffSiQX46kAzfjal8KT4u+2n1tPfpalqNc7U0ZA30r9Olv82IacvukbJQDeQr9FozqtfB73xPI877rgDH3/8Mb799lsMGjTI5/uTJ0+GWq3GN998Iz129OhR1NTUYPr06T/16Z4U2mWDydpiHHrGHG0y4ZIXNuHLfY29PS0AQJNsGNkPFW1oMUU2nCyYDtmQN0DYNQzE9p6rxPVco/O8lRSpCcLxDNbwg95Yf3hthxU8712HJh+8pY9jwaJTmjqelywEA2PEn3kgyK5xduyspNCZ6MGZOuSnxMPh8mCL2BcfDJtuXpAaL+32lpPvE2fDySYXpwY8r9PqQHM3J/7s6ANyANLecLYbHOjNoDdx+nqYlWg9KUgTzudwoxFucWd8SpAVdLGSTwvvy+nmWUlx+OjXM3HNGUV9dkxCCCGEEPLT69egfOnSpfjvf/+Ld955B0lJSWhqavr/9u49Oqry3hv4d889k0yukysQAiaACEQuEqPiBRDkoNXWqrXUF6nVqmil2J5Xeyzoebuqbdexaku1b/taPT1ttVqt9YZSwFAVuQSQm1zlpuRCCEmGyW0uz/vH3s+emWSSyUwCsxO+n7VYi8xMNnvCs1h85/d7fg9qa2vR1qb+Zz0jIwN33HEHli5dirVr16K6uhqLFi1CZWVl1CFvFAqrQP9D+XNVB7H9i2Y8+Mo2tV28n8InhAcF8I9txxO+lpyynq2FcTl9Pd4j0Tr8Af1nVpAemsKcrQ0+k+dW90SeJX7sVBvqPR1o8wVgUkLHcAGh4NjuC+ofAMhgKs9Y33m8eyiX185P73k6tKIouHyMWmGv2nuix9fJ0JuZEn1ydXgolyGyfERGt7kEp7w+1GlZOtbk9Z4M0yZpyyn0waBAS3vkMWZ9JY9Ek+eUCyHQIt+rs29Tukdof1e7a9RqdqrNDLulHwMPunC7QvcxUC3xRERERDR0JDWUP/vss2hubsaVV16JwsJC/dfLL7+sv+aXv/wlrr32Wtx44424/PLLUVBQgNdeey2Jd21scno0kPiZ3QDQ1hnA+7vUYXrtviC+95et6PDHOXq6i5pmNYTJ48Ze+PgwOv29Dyfr+VpqwJdhMidVhvL4Poiob1Ffb7OYIqqjMtCd6qVS7gsE9c6EE54O7NKC9fAsZ0Q1WgZHQK3GAqFgKs8a31vrifj5hl+7t0o5AP3YrnX7egnl2vvoqXqcG1bNld0HLocVY7S/K6tZTeeNrZ1oaFd/Pyo3sbOx5XuXXQOedj/k6WgJH4mmnVPe7guiMxCM61rhZ1YDfQ/zfSV/tiNznEi1G2c7CREREREZQ9Lb16P9uv322/XXOBwOrFixAo2NjfB6vXjttdd63E9OA9e+vmZPPbydARSkO5DltGJ3TQvW7uk59PWFbF9fPLMUuS47vjjVhleqjyV0rX11auW+LE8NjfLs4pNxfhAhW+jz0+0Rrdiyfd3T7odPC3mf1bTgP9/cDa8WALv+fN/fVQcAOL8wshpqMZv0M5H31KqhvDRPrTIPz0pBptMKX0BgX22oG0FWya1mRb+XnlxS6obZpODzBi8OaeeHdyVbuntqy45WKQeA2y8pQXG2E9+4SG2RPuXtRJP2Ix6Wmdh+3KIulXJZxXdYTXFXqNMcslKuXqOpTb05i0nRf+ax5KU7cO2kQv3r8PPTB8KkEZlQlNDZ70RERERE4ZIaymngNYaF0ngDarh/fPolAOCrU4bhqnHq+dfybOpEHddC+Wh3Ku69Up2Q/us1B9Dui78Cv69OvZeyfDXcyiDZ3OZDS7tPr0jHUqdVyvNdkS3iGSlW/YgpGWjnPf0vPP/RIfz+X+qUcllll1btVkP5+MLuZ7DLim5QAIoCvQKtKIrewv7xwQb99fVaKM9Ns8MU42y7dIcV07T93zc99zFW7uw+A0Dfs91DKE+1W5BiVUNseCj/xvRirPv3qzB9VDYAtZ2/qVO9n4Je2up7I1v3m1p98Hb4E95PDoS2Bsj29fBrxbPf/YkbJ+m/zxjgSvmU4ixs/NFs/OdXJgzodYmIiIhoaGAoH2IGYk/56Q6/XhX/SnkRRuWobcqHGvp+1l5Xnf6gfj+FGQ7cOr0YBekO1DS3Y+2e+riu1drpx9FG9V5kK3xGihVmLbze9+etmPf0v3pt55Zk9b7rvm2zSdEHtDW1duqt1kCoKisHsUmySyF8YJwkwyOgtkuHtzH/20S1Svt/132uV+HltXP7GHx/csMElOWloeF0J+790xbsrY38AKWpD8FX3wqQ1j2Uyr37jd5ONGvLqijBSnm6w6r/PI43tenroqf97r1fSxv0pv3c4h3yJqXZLVjz4BWYOS4PP5w7Ju77iCXXFfvDFSIiIiI6NzGUG0ynP5jwPmtgYNrXj5z0ojMQRE6qDeMKXChxq6H88Ekv9tS24IJlK/HrNfvjumZdSzuEAGxmE7JTbXBYzagYrVZfj52KL+zv11rX3Wk2/Yxsk0nRg6MM4x8eaIh+gfD78kQP5QCQ5QwNe1u5s1Z/3Km1RctqdlfjY4TyrsO+vj51OEpynDjp7cTzHx6KuHZ+jP3kUlm+C29/bwZmjstDUAD/9f7eiOebW3sf9AaE9laP6LLHGgi18x891QafUMNlXnp855OHCx/2tvWoeiRdtJ9bLGl2bU9510p5AtPTR+em4fnbL8LMcbHPNiciIiIiGigM5QYSFMBXfrMec59ap+9jjldkpTyx9nUZ4LJTbVAUBaNkKG/w4p3tNfB2BvBWnMek1WqV34IMh95WLNufa5vj+/BAtq7LFnBJHosmRTtmrCvZgp4fJWBmpYaGvYWHcnlud33Yvm8p02lFYUb3gC/b14Hux2JZzSYsnTMWgFotb+3044Q8Di2O4GuzmPCjfxsHkwK8v7tOD7tA2PT1XsLq/7lhAn7+9UmYUdp977OsnssPjHJSbf2aUC5D+aEGLzYebgQAXFSSHfd1uh6J1pxgpZyIiIiIKFkYyg3kVAdw8IQ6rKungV2xDET7+qkuQ8Fkpfykt1OvPn/e4NXPdO4L2f4dHlgLtN/XtrRF/Z6e9BTKc7tUlXcdb4k5tEu2iUevlKtBdH+dB5uONOqPt3aqAfCEVmWXE9QBYHxhetS9zOlhIbHrIDgAuHZiIdxpdng6/Nhfdxpf6D+v+FrES/Nc+NqU4QCA33xwUH9cDkDrLayOcqfi5mkjorZZdw3z0T54iMc0LYC/u6MW2441AQCmj+p+LnosctBbZyCI1k5/2NFvDOVERERENDgwlBuIPGoKQLc9wX0VHsoTHfQWCnBqKE2zW/TAu+VoEwC1YvpFHG3ncu92RCjXK+XtUb+nJ3u19vVYlfLmNh++ONV74K/tJZTLIPrali8Rnu1Pd6iVcjkkbmpxKEyOL4zegh3Zvt79NSaTgtG5oW0Ch7UPZUpy4j927PZLSgAAnxw8iaD2wUmie60lu8UccbRbQT9a1wHo0843Hm5Euy+I7FRbQueeu+wWfU39rfqLfg2NIyIiIiJKBoZyAzkRlk0TCeVtnQG0hU0yb27zJbQ/XQa4rLDq6Kgo4fBA/eluj/VEniteGDYcLF8LU3Utcbavaz+bsQWRIS4nbGq4DGW7jvc+hb3X9nWtUv65FpDlVPJWbaiYPE5tysiwUN7DvmgZylOs5m7nYkvyZ3y4oVXvlChxR39tb8YVuOC0meHp8GNfvQftvgA6tHXQW/t6LNlhH3oU9LNSPiLbifLhoQ6DaSOz4pqWLimKok/y/9WaA9ivnRAw0BPUiYiIiIjOFIZyAzkRVinfk0AoP+lVA6bNbIJFa0GWj8WjqVWtlIcHuGjh8OCJvoVyIYReVY9WKa9radcrurE0t/n06nZZl0q5DM15LjvmjFeHde063vO+8tMdfn1qd2+D3qTLx6h7rb1a+7oM9MXZTox2p0JRgMnF0Vuw5ST3MQWuHqdwj9R+xp9+0aRvIUikUm4xmzC5OBMAsPnwKb16bDYpEdXueGWFh/IEj0MLd+2kIv338si1RNx80QgMy0xBvacD78mz4gu6bxEgIiIiIjIihnIDaQivlNf17ZztcLJ1PSfNplc1E2lhb9L3lIdC2Ch3qCotM2VfKuWbDjdi2k/+iX9+ph57Fr5HOtdlh0kB/EERMTW+N3u088eLMhx60JVkEL2uvEjf591bpVzuJ3fZLRFHlEmZXaqtl5flAgC8HQEEgkLfs5/nsuP3C6fhT9+p0IfidSUr6JeXdR+iJslKuTyvPM9lj3pffTF1pBpyq4+cimhdT6QaLWWHfUjR30o5AMzXWtiBxIa8SXaLGUtmlwFQOxEemX8+rplQ0O/7IyIiIiI6GxIvm9GAC99TfqyxDd4Of1yhTAbbLKcNAup08BMJDHtrijKpe1RYpfzSUjf+tb+hT6H87e01OOnthM1swrSSLFw8OhS+rGYT3Gl21Hs6UNvc3m1QWzQ7tInqF4QNV5MuHp2DD//3VchPd2D7F00Aep/AXhdjwnl4u/aY/DS9yu/t9OOktwNBoX5AkZNmR166A6N72RN91dg8bPqP2XBHOQNcGqmF8naf2mreU8Dvi6laS331kVMDNvwsOzX0c+rvnnJAPef84Xnj0HC6A5OGd//7jMdN09RqeYk7NeHz04mIiIiIkoGh3CCCQaFXyq1mBb6AwL46T4/t0NE0ng5VyqXEKuVa+3rYmdYlYQHxxinD9VAuhOi1+irb1pddNx7funhkt+cLMhxqKG9px0TEDmYyZE+MEsoBYHiW+uHB+YXpMJsU1Hs6cKDeg9K87u3Mof3k0au+4R9KTCvJ1j8g8Xb49e/NSbPD3EM7elexPnToukWgP6F8cnEmFAU42tiq77NO73coH9hKOQB894rzBuQ6AHBJlKPciIiIiIiMju3rBlHn6YBfKLCaFUzTWo/jHfYm29ezU23I1fZXJ3IsWrRBbyU5qWolMseJq8fnw6QALe3+mJV4Of18eFb06qUMxHKfeCw7tXb0nkK55LRZMHNcHgDgfz45GvU1+tnpPYTyrLD29Wkjs+C0qedyt3YEcMITal0fKE6bJeJ6Jf0I5ekOK8Zqe+5Xa1sH+jPkDeiyp3wA3zcRERER0bmModwgDp9Up20Pz0zR9x/vretbKG/0duKJd/eg+sgpAGool9XyBk/8oVwOGcsIC3EOqxnvPDAD/7j/MqTaLRihTRDvrYVdCIFjjWqlfEQPE8dlS3hdH45F83b49eFyE2KEcgC4TavM/636C/1s8XCh9vW+hPJsfUiat9OPU62hD0AGUngQT2TIWzjZwv6RdrZ8f9vX5ZFzaRYBu9Xcr2sREREREZGKodwgjpxUK8rFOU69wrmnpm+h/L/e34vnqg5i5a5aAGp4cidYKRdCoLkttDc9XEaKVR+uJs+UPtzQ81nlTa0+eDvVI9qG9bDPN55K+Wc1LRBCPb6sL/vPLyt1oyTHCU+HH29sO97t+d6OQ5OPzxqXh2snFWJEdgqcWigPitD9DvR52CU5oQ8v+tO+DgDTStRQLo9D6++95rnUv6tsFsmJiIiIiAYM95QbxBGtojwy26lXynceb+5xz7YQAh3+IPxBgb9v/TLiuaxUG3K0oVzbtT3YVftOoK3Tj2smFHa7VrjWzgB8AfV4st7anWXVtKmt5z3rx7T95HkuOxw9VFZl63htL5XyVbvr8KPXd+C8XDWkxmpdl0wmBd+6eCR+8vZn+N26z/H1qcNhNYc+h4rVvq4oCv7f7RfpXzvD3sOXWlt+f1vCuwqvlI/Mif+M8nByG4TU37O7Lytz494rRsPSsK9f1yEiIiIiohBWyg3iyEk1wJbkODEm3wWbxQRPu19/vKvFf96Ci37yTzz6j13wdgYiqr15LgcuLc2BxaTg8xNebPj8JO54YRPu/p8t2Hy4sdf7kG3ZNrMJKb20KMuhYS1t3dvCpVj7yYHQwLDeKuWvVh/DCU8HPvlcvfe+tK5Lt1w0AjmpNnze4MVLm45FPBerfb0rk0nR95Ufb9JCecoAt69rLetFGY4eP8joq+FZKREdBf1tX7eaTfj+7FKcl96vyxARERERURiGcgOxKAIjc5ywWUw4v0BtYZdHgLX7Arj7j9X4qxYs39lRC0+HH69WfwEA+M5lo/GHRRfh9ktKcMWYXLgcVlRox4/94NVP4Q+q1e+fvP0ZhBDd/uxgUOCzmhZ9WFyms/czrWUbuzxuK5pY+8mBUCgP31N+uiMy6HvaI7/ua6UcAFwOKx7QzrB+atU+1DSrYVoIEbN9PRqnTW0u+bLpzFTKLy11Y9rILHz7slH9vpaiKJg2MjS9f6Bb7YmIiIiIqP8Yyg3iN9+8EL+oCOASLUhP1M5tlqF846FGrNxVi2erDiIQjAzVNrMJN04djqvG5uHRr1wAm0X9a505Lh+Aeua5tO1YE97aXtPtz//v9Ycx7+l/4clVamtyrLCZkaKG05b2nkN5nyrlWpXa0+GHp92HFWsPYNKj7+GFjw7pr6nRAnv5iEzMKHPjkvPiO/rq1unFGO1OxUlvJyofX4P/9fxGnPB0oDOg7rWWe6X7ItWuVq9l+/pAB92MFCtevecSfGfG6AG53tSwUD7QHyAQEREREVH/MZQbiEkBLNqeZ1kN3vGFGsplBbvB06G3mAPAd68YjV/cNCnqFPBZ2pFgAOCyW3C3dib0U//c161avrtGPWqsat8JALHbskPt672Fcq1SntVzpTzVbtHv/VhjGz7c34CgAB57azfe/PQ4hBB6q/ivvjEZf7yjAim2+Nq6rWYTnrl1MspHZAIA1u07gVWf1QFQ98bLDzH6ItUmJ7CrA+y6DsMzGoZyIiIiIiJjYyg3KLlveufxZgSDQg/lng4/appCk78fnnc+rr9wWNRrlLhT9eFo108uwuKrzkOqzYyDJ7z46MDJiNfWa0enyaweK8DJ9vXeQvkxvVLe+8CyYq29/WijF0e0o+GEAH7wyqc41ODVp4fnZyQ+9nvCsAy8sfhSXFqaAwD4+KD6/vu6n1ySlXLJ6EH3gqIMfTaAHP5HRERERETGwVBuUBHD3hpb0RRWHd+nnV+e04czsn8wZywuLc3BPVeWwuWw4utThwMAXlx/OOJ1cn+1FLN9XXu+pT36oDchRKhSnt1z+zoQCuX7606jRhu+lum0osMfxD+1irY7zQa7pf9nY5dqR7mt10J5PPvJgdCecsnoodxmMeHJm8vx0LxxEZPdiYiIiIjIGBjKDcpqNuH8QnXM9Y4vm9EYHsrr1VAerWW9q3kTC/Gn71ysnxN+W2UJAGD1Z3X6IDYgVCmXYrVlR6uUv7+rFjN+vgbv7apFw+lOtPuCUBSgMKNvofyjgw0QAkizW/TjvNbtawAQ+xp9dV6eGspl50FPx6H1JM0eGcozBnj6+pkwb2KhvnWBiIiIiIiMhaHcwMbmqwHySIMXp1pD4Xd/3WkAfQvlXZXmpWFGmRtBAfx1szrJ3R8I4qQ3MpRnxGpfDxv0Jven//f6IzjW2Ibv/rEaP3jlUwBq4I61Z7tYO4+7+sgp/XvGaO99o3aEW2FGfOG5J7JSLsXbvu7ssp+dE82JiIiIiKg/GMoNTE4Fr/d04JQ3Svt6WmJVWtnC/tb2GgghcNLbia6npMUc9KZVyn0BgTafOvSsU9v7DagD46xmBT+ePz7m/chKuS+g3kSJ24kyLZTLaxZlDkylvDQvMpTH276eGlYpT7WZ4xoSR0RERERE1BUThYHlaYGx3tMeUSmXR40lUikHgNnn58NhNeFQgxe7jregTtvH7bCGlkNWjEq502aGxaSeY97Spu4rbzgdqranWM347W1TMXt8fsz7GZkTOQiuODsVZXmuiMcGqlKe67LD5QgF63jb18MHvWUafPI6EREREREZH0O5geW51FB+okulXMpOcJp2qt2CWdoZ5m9+elwf8laW50KRFn7drt6vrShK6Fg07axyGe5Xff9ybPyPWfo56bHkuxwRFeeSHCfOy02DooReUzhAlXJFUSKq5flxt6+HAj1b14mIiIiIqL8Yyg0sN7x9vbV7KO/L9PWeXFdeCEBtYa/zqGE6z2XHz74+Cd+bWYopxVm9fTsAIF2rOLe0+eBp9+lndxdlpsDl6HtgNZkUjMgKhe7iHCdSbOaI882LBqhSDkTuK8+Lt33dFl4pZygnIiIiIqL+YSg3MFkpr2lu18/qDpdo+zoAXDk2D6k2M75sakPV3hPqn5dux4yyXCydMxZmkxLjCtAr5c1tPtRp1XaX3RKx77qv5L5yACjJUY/uKguraA9UpRwITWA3mxS44+w2CH9vDOVERERERNRfDOUGlquF8kBQRH2+P6HcYTVjyki1Gl6174T258VXjc4Ia1+v11rX4608SzKU2ywmfZ93Wb66r9ykAPkx2unjISvleS47TH348CFceCgfDMehERERERGRsTGUG5jDatZbxKNJdPq6JFvUZRU+L87gGzqr3K+3wMe7R1sq1qrjxdlOPSjLSnmeywGLeeCW6qWlbswoc+OOy0bF/b2slBMRERER0UCKv8+Yzqq8dAda2tVzybOc1ogp7P2plAPQK+X6nxVvKNfOKm9u86FV208e7zRzaXpJNhQFuHh0tv5Y5Xk5cNktuHyMO6Fr9iTFZsYf76hI6HvD95THmlBPREREREQUC0O5weW57DhQr4by83LTsPnIKQDq3m27xdzbt8Z04YjMyD8rzkAdqpT74Nda7OO9hjRxeAaqH7kamWETzYsyU1D946thNcfXYn4mhU9fj3WWOxERERERUSxsXze48Oq1O82uV2ez+9m6Dqh7wsOHqcVfKQ/bU663rye+9zs71dZtj7fNYoKiGCeUp4XvKWelnIiIiIiI+omh3OByw4JyVqoNOWnq1/1tXZfCjz5zpyUYytv8+vT1RPeUDxZOe9iRaDynnIiIiIiI+omh3ODywiaiZzmtcGsV8v6cUR5uqravPDvVBpslvuUgh9A1t/lQ29y/QW+DRWp4+7qT7etERERERNQ/DOUGF37EWPYZqJRfPiYXLrsFleflxP294eeUD0T7+mDgsJqQajNDUSK7GIiIiIiIiBLBQW8GFx78Mp02FGqV6IGqSBdkOLDpkdmwx1klB0LnlB9tbIUvoA56G+pBVVEUrFgwBZ52/4B9MEJEREREROcuhnKDC29fz061YtFlo2CzmPCti0cO2J/hsCY2xV1OXz/d4dfuz9bvifCDwZVj85J9C0RERERENEQwlBtc10r5sMwU/Ps145J4RyHynHKpKHNo7ycnIiIiIiIaaNxTbnDpDgtc2kA1ow1Rk5Vy6b6rypJ0J0RERERERIMTK+UGpygKnrrlQtQ0t2NYZkqybyeCw2pGcbYTX5xqxW8WTME1EwqSfUtERERERESDCkP5IDDr/Pxk30KP3lh8KTr8QRRkGKuKT0RERERENBgwlFO/ZHECORERERERUcIGxZ7yFStWoKSkBA6HAxUVFdi4cWOyb4mIiIiIiIio3wwfyl9++WUsXboUy5cvx5YtW1BeXo65c+eivr4+2bdGRERERERE1C+Gb19/8sknceedd2LRokUAgOeeew5vv/02nn/+eTz00EPdXt/R0YGOjg7965aWFgCAz+eDz+c7OzedAHlvRr5HIq5TMjquURoMuE7J6LhGyegGwxqN594UIYQ4g/fSL52dnXA6nXj11Vdxww036I8vXLgQTU1NeOONN7p9z6OPPorHHnus2+N//vOf4XQ6z+TtEhEREREREaG1tRXf/OY30dzcjPT09F5fa+hKeUNDAwKBAPLzI6eP5+fnY8+ePVG/5+GHH8bSpUv1r1taWjBixAjMmTMn5g8jmXw+H1atWoWrr74aVqs19jcQJQHXKRkd1ygNBlynZHRco2R0g2GNyo7tvjB0KE+E3W6H3W7v9rjVajXsX1i4wXKfdG7jOiWj4xqlwYDrlIyOa5SMzshrNJ77MvSgN7fbDbPZjLq6uojH6+rqUFBQkKS7IiIiIiIiIhoYhq6U22w2TJ06FatXr9b3lAeDQaxevRr33Xdfn64ht8zH0z6QDD6fD62trWhpaTHspz1EXKdkdFyjNBhwnZLRcY2S0Q2GNSrzZ19GuBk6lAPA0qVLsXDhQkybNg3Tp0/HU089Ba/Xq09jj8Xj8QAARowYcSZvk4iIiIiIiCiCx+NBRkZGr68xfCi/5ZZbcOLECSxbtgy1tbW48MILsXLlym7D33pSVFSEY8eOweVyQVGUM3y3iZMD6Y4dO2bogXR0buM6JaPjGqXBgOuUjI5rlIxuMKxRIQQ8Hg+KiopivtbQR6KdS1paWpCRkdGnkflEycJ1SkbHNUqDAdcpGR3XKBndUFujhh70RkRERERERDSUMZQTERERERERJQlDuUHY7XYsX7486hnrREbBdUpGxzVKgwHXKRkd1ygZ3VBbo9xTTkRERERERJQkrJQTERERERERJQlDOREREREREVGSMJQTERERERERJQlDOREREREREVGSMJQbxIoVK1BSUgKHw4GKigps3Lgx2bdE54h169bhuuuuQ1FRERRFwd///veI54UQWLZsGQoLC5GSkoLZs2dj//79Ea9pbGzEggULkJ6ejszMTNxxxx04ffr0WXwXNJQ9/vjjuOiii+ByuZCXl4cbbrgBe/fujXhNe3s7Fi9ejJycHKSlpeHGG29EXV1dxGuOHj2K+fPnw+l0Ii8vDz/84Q/h9/vP5luhIezZZ5/FpEmTkJ6ejvT0dFRWVuLdd9/Vn+caJaN54oknoCgKlixZoj/GdUrJ9Oijj0JRlIhf48aN058fyuuTodwAXn75ZSxduhTLly/Hli1bUF5ejrlz56K+vj7Zt0bnAK/Xi/LycqxYsSLq8z//+c/xzDPP4LnnnsOGDRuQmpqKuXPnor29XX/NggULsGvXLqxatQpvvfUW1q1bh7vuuutsvQUa4qqqqrB48WJ88sknWLVqFXw+H+bMmQOv16u/5vvf/z7efPNNvPLKK6iqqsLx48fxta99TX8+EAhg/vz56OzsxMcff4wXX3wRL7zwApYtW5aMt0RD0PDhw/HEE0+guroamzdvxsyZM3H99ddj165dALhGyVg2bdqE3/72t5g0aVLE41ynlGwXXHABampq9F8ffvih/tyQXp+Ckm769Oli8eLF+teBQEAUFRWJxx9/PIl3ReciAOL111/Xvw4Gg6KgoED84he/0B9ramoSdrtd/OUvfxFCCLF7924BQGzatEl/zbvvvisURRFffvnlWbt3OnfU19cLAKKqqkoIoa5Jq9UqXnnlFf01n332mQAg1q9fL4QQ4p133hEmk0nU1tbqr3n22WdFenq66OjoOLtvgM4ZWVlZ4ve//z3XKBmKx+MRZWVlYtWqVeKKK64QDzzwgBCC/5ZS8i1fvlyUl5dHfW6or09WypOss7MT1dXVmD17tv6YyWTC7NmzsX79+iTeGRFw6NAh1NbWRqzPjIwMVFRU6Otz/fr1yMzMxLRp0/TXzJ49GyaTCRs2bDjr90xDX3NzMwAgOzsbAFBdXQ2fzxexTseNG4fi4uKIdTpx4kTk5+frr5k7dy5aWlr0SibRQAkEAnjppZfg9XpRWVnJNUqGsnjxYsyfPz9iPQL8t5SMYf/+/SgqKsLo0aOxYMECHD16FMDQX5+WZN/Aua6hoQGBQCBi8QBAfn4+9uzZk6S7IlLV1tYCQNT1KZ+rra1FXl5exPMWiwXZ2dn6a4gGSjAYxJIlS3DppZdiwoQJANQ1aLPZkJmZGfHarus02jqWzxENhB07dqCyshLt7e1IS0vD66+/jvHjx2Pbtm1co2QIL730ErZs2YJNmzZ1e47/llKyVVRU4IUXXsDYsWNRU1ODxx57DDNmzMDOnTuH/PpkKCciokFj8eLF2LlzZ8QeMyKjGDt2LLZt24bm5ma8+uqrWLhwIaqqqpJ9W0QAgGPHjuGBBx7AqlWr4HA4kn07RN3MmzdP//2kSZNQUVGBkSNH4q9//StSUlKSeGdnHtvXk8ztdsNsNnebHFhXV4eCgoIk3RWRSq7B3tZnQUFBt6GEfr8fjY2NXMM0oO677z689dZbWLt2LYYPH64/XlBQgM7OTjQ1NUW8vus6jbaO5XNEA8Fms6G0tBRTp07F448/jvLycjz99NNco2QI1dXVqK+vx5QpU2CxWGCxWFBVVYVnnnkGFosF+fn5XKdkKJmZmRgzZgwOHDgw5P8dZShPMpvNhqlTp2L16tX6Y8FgEKtXr0ZlZWUS74wIGDVqFAoKCiLWZ0tLCzZs2KCvz8rKSjQ1NaG6ulp/zZo1axAMBlFRUXHW75mGHiEE7rvvPrz++utYs2YNRo0aFfH81KlTYbVaI9bp3r17cfTo0Yh1umPHjogPkFatWoX09HSMHz/+7LwROucEg0F0dHRwjZIhzJo1Czt27MC2bdv0X9OmTcOCBQv033OdkpGcPn0aBw8eRGFh4dD/dzTZk+ZIiJdeeknY7XbxwgsviN27d4u77rpLZGZmRkwOJDpTPB6P2Lp1q9i6dasAIJ588kmxdetWceTIESGEEE888YTIzMwUb7zxhti+fbu4/vrrxahRo0RbW5t+jWuuuUZMnjxZbNiwQXz44YeirKxM3Hrrrcl6SzTE3HPPPSIjI0N88MEHoqamRv/V2tqqv+buu+8WxcXFYs2aNWLz5s2isrJSVFZW6s/7/X4xYcIEMWfOHLFt2zaxcuVKkZubKx5++OFkvCUagh566CFRVVUlDh06JLZv3y4eeughoSiKeP/994UQXKNkTOHT14XgOqXkevDBB8UHH3wgDh06JD766CMxe/Zs4Xa7RX19vRBiaK9PhnKD+NWvfiWKi4uFzWYT06dPF5988kmyb4nOEWvXrhUAuv1auHChEEI9Fu3HP/6xyM/PF3a7XcyaNUvs3bs34honT54Ut956q0hLSxPp6eli0aJFwuPxJOHd0FAUbX0CEH/4wx/017S1tYl7771XZGVlCafTKb761a+KmpqaiOscPnxYzJs3T6SkpAi32y0efPBB4fP5zvK7oaHq29/+thg5cqSw2WwiNzdXzJo1Sw/kQnCNkjF1DeVcp5RMt9xyiygsLBQ2m00MGzZM3HLLLeLAgQP680N5fSpCCJGcGj0RERERERHRuY17yomIiIiIiIiShKGciIiIiIiIKEkYyomIiIiIiIiShKGciIiIiIiIKEkYyomIiIiIiIiShKGciIiIiIiIKEkYyomIiIiIiIiShKGciIiIiIiIKEkYyomIiM5ht99+O2644YZk3wYREdE5y5LsGyAiIqIzQ1GUXp9fvnw5nn76aQghztIdERERUVcM5URERENUTU2N/vuXX34Zy5Ytw969e/XH0tLSkJaWloxbIyIiIg3b14mIiIaogoIC/VdGRgYURYl4LC0trVv7+pVXXon7778fS5YsQVZWFvLz8/G73/0OXq8XixYtgsvlQmlpKd59992IP2vnzp2YN28e0tLSkJ+fj9tuuw0NDQ1n+R0TERENPgzlREREFOHFF1+E2+3Gxo0bcf/99+Oee+7BTTfdhEsuuQRbtmzBnDlzcNttt6G1tRUA0NTUhJkzZ2Ly5MnYvHkzVq5cibq6Otx8881JfidERETGx1BOREREEcrLy/HII4+grKwMDz/8MBwOB9xuN+68806UlZVh2bJlOHnyJLZv3w4A+PWvf43Jkyfjpz/9KcaNG4fJkyfj+eefx9q1a7Fv374kvxsiIiJj455yIiIiijBp0iT992azGTk5OZg4caL+WH5+PgCgvr4eAPDpp59i7dq1UfenHzx4EGPGjDnDd0xERDR4MZQTERFRBKvVGvG1oigRj8mp7sFgEABw+vRpXHfddfjZz37W7VqFhYVn8E6JiIgGP4ZyIiIi6pcpU6bgb3/7G0pKSmCx8L8WRERE8eCeciIiIuqXxYsXo7GxEbfeeis2bdqEgwcP4r333sOiRYsQCASSfXtERESGxlBORERE/VJUVISPPvoIgUAAc+bMwcSJE7FkyRJkZmbCZOJ/NYiIiHqjCCFEsm+CiIiIiIiI6FzEj6+JiIiIiIiIkoShnIiIiIiIiChJGMqJiIiIiIiIkoShnIiIiIiIiChJGMqJiIiIiIiIkoShnIiIiIiIiChJGMqJiIiIiIiIkoShnIiIiIiIiChJGMqJiIiIiIiIkoShnIiIiIiIiChJGMqJiIiIiIiIkuT/AwzHjAL6t8bvAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the data\n",
+    "plt.figure(figsize=(12, 3))\n",
+    "plt.plot(t, data, label=\"y_values\")\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"y_values\")\n",
+    "plt.title(\"y_values over time\")\n",
+    "plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now inspect the time series and identify the jump at $t=270$. We can also see that there is a clear trend and seasonality. We will now use the PSD to identify the frequency of the seasonality and the trend.\n",
+    "\n",
+    "remember that to compute a PSD we need to make our time series stationary. In the cell below I have shown how not to do it. without stationarity, the PSD is not useful. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.psd(data, Fs=1);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we already now there is a linear trend and a jump we can remove them from the time series. We can do this by fitting a linear model to the time series and subtracting it. The resulting residuals will be stationary and we can compute the PSD.\n",
+    "\n",
+    "Matrix A is defined as follows:\n",
+    "\n",
+    "$$\n",
+    "A = \\begin{bmatrix}\n",
+    "1 & t_1 & 0 \\\\\n",
+    "\\vdots & \\vdots & \\vdots \\\\\n",
+    "1 & t_{269} & 0 \\\\\n",
+    "1 & t_{270} & 1 \\\\\n",
+    "1 & t_{271} & 1 \\\\\n",
+    "\\vdots & \\vdots & \\vdots \\\\\n",
+    "1 & t_{500} & 1 \\\\\n",
+    "\\end{bmatrix}\n",
+    "$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Frequency corresponding to the highest peak: 0.084\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "offset = np.zeros_like(data)\n",
+    "offset[270:] = 1\n",
+    "\n",
+    "A = np.column_stack((np.ones_like(t), t, offset))\n",
+    "xhat = np.linalg.solve(A.T @ A, A.T @ data)\n",
+    "yhat = A @ xhat\n",
+    "ehat = data - yhat\n",
+    "\n",
+    "fig, axs = plt.subplots(2, 1, figsize=(12, 5))\n",
+    "\n",
+    "# Plot data and fit\n",
+    "axs[0].plot(t, data, label='Data')\n",
+    "axs[0].plot(t, yhat, label='Fit', linestyle='--')\n",
+    "axs[0].set_xlabel('Time')\n",
+    "axs[0].set_ylabel('y_values')\n",
+    "axs[0].set_title('Data and Fit')\n",
+    "axs[0].legend()\n",
+    "axs[0].grid()\n",
+    "\n",
+    "# Plot residuals\n",
+    "axs[1].plot(t, ehat, label='Residuals', color='red')\n",
+    "axs[1].set_xlabel('Time')\n",
+    "axs[1].set_ylabel('Residuals')\n",
+    "axs[1].set_title('Residuals')\n",
+    "axs[1].legend()\n",
+    "axs[1].grid()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Create a sample signal\n",
+    "N = len(data)  # Number of points\n",
+    "T = 500/N  # Sampling interval (1000 Hz sampling rate)\n",
+    "\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "# in plt.psd we have added NFFT=1024 and pad_to=2048 to get a better resolution\n",
+    "pxx, freqs, line = plt.psd(ehat, return_line=True, Fs=1, NFFT=1024, pad_to=2048)\n",
+    "\n",
+    "plt.xlabel(\"Frequency\")\n",
+    "plt.ylabel(\"Power Spectral Density\")\n",
+    "plt.title(\"Power Spectral Density of the residuals\")\n",
+    "plt.grid(True)\n",
+    "\n",
+    "\n",
+    "# Find the frequency corresponding to the highest peak\n",
+    "highest_peak_freq = freqs[np.argmax(pxx)]\n",
+    "print(\"Frequency corresponding to the highest peak:\", round(highest_peak_freq,3))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using peak detection we can identify the frequency of the seasonality and the trend. \n",
+    "\n",
+    "Finally, we can use the identified frequencies to model the time series.\n",
+    "\n",
+    "For this, we need to expand our matrix A to include the seasonality and the trend. The new matrix A is defined as follows:\n",
+    "\n",
+    "$$\n",
+    "A = \\begin{bmatrix}\n",
+    "1 & t_1 & 0 & \\cos(2\\pi t_1 f) & \\sin(2\\pi t_1 f) \\\\\n",
+    "\\vdots & \\vdots & \\vdots & \\vdots & \\vdots \\\\\n",
+    "1 & t_{269} & 0  & \\cos(2\\pi t_{269} f) & \\sin(2\\pi t_{269} f) \\\\\n",
+    "1 & t_{270} & 1  & \\cos(2\\pi t_{270 f}) & \\sin(2\\pi t_{270} f) \\\\\n",
+    "1 & t_{271} & 1  & \\cos(2\\pi t_{271 f}) & \\sin(2\\pi t_{271} f) \\\\\n",
+    "\\vdots & \\vdots & \\vdots & \\vdots & \\vdots \\\\\n",
+    "1 & t_{500} & 1  & \\cos(2\\pi t_{500 f}) & \\sin(2\\pi t_{500} f) \\\\\n",
+    "\\end{bmatrix}\n",
+    "$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = highest_peak_freq\n",
+    "\n",
+    "A2 = np.column_stack((np.ones_like(t), t, offset, np.cos(2*np.pi*f*t), np.sin(2*np.pi*f*t)))\n",
+    "\n",
+    "xhat2 = np.linalg.solve(A2.T @ A2, A2.T @ data)\n",
+    "yhat2 = A2 @ xhat2\n",
+    "ehat2 = data - yhat2 # removing the functional components from the data\n",
+    "\n",
+    "fig, axs = plt.subplots(2, 1, figsize=(12, 5))\n",
+    "\n",
+    "# Plot data and fit\n",
+    "axs[0].plot(t, data, label='Data')\n",
+    "axs[0].plot(t, yhat2, label='Fit', linestyle='--')\n",
+    "axs[0].set_xlabel('Time')\n",
+    "axs[0].set_ylabel('y_values')\n",
+    "axs[0].set_title('Data and Fit')\n",
+    "axs[0].legend()\n",
+    "axs[0].grid()\n",
+    "\n",
+    "# Plot residuals\n",
+    "axs[1].plot(t, ehat2, label='Residuals', color='red')\n",
+    "axs[1].set_xlabel('Time')\n",
+    "axs[1].set_ylabel('Residuals')\n",
+    "axs[1].set_title('Residuals')\n",
+    "axs[1].legend()\n",
+    "axs[1].grid()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "TAMude",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
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diff --git a/book/time_series/forecasting.md b/book/time_series/forecasting.md
index f515eea28dbc0495c68cbc20d9bf3d9c8ad98913..8785627497147ac9dd078c72c3a8294fe8ce5270 100644
--- a/book/time_series/forecasting.md
+++ b/book/time_series/forecasting.md
@@ -8,13 +8,11 @@ The material on this page is provided to give you extra insight into time series
 
 Time series analysis is about analyzing time series of data points of a variable, with the goal to extract meaningful characteristics and statistics of the data, e.g., to study the trend and seasonality. Very often we do so in order to be able to predict future values based on the previously observed ones, which is referred to as **forecasting**.
 
-In this part of the book, you first learned about the different types of time series ([Chapter 4.2](timetypes)) as well as the components that can be distinguished ([Chapter 4.1](components)). Some of the components may relate to the *signal-of-interest*. However, another important component that we have to deal with is the noise.
+In this part of the book, you first learned about the components that can be distinguished ([Chapter 4.1](components)) in time series. Some of the components may relate to the *signal-of-interest*. However, another important component that we have to deal with is the noise.
 
 Modelling and estimating the signal-of-interest ([Chapter 4.3](modelling_tsa)) using the concepts of observation theory. The remainder of this chapter focused on the noise modelling. In order to do so, we need to work with *stationary*, i.e., time series of which the statistical properties do not depend on the time of observation ([Chapter 4.4](stationary)). An example of a stationary time series are the residuals after best linear unbiased estimation. Using these residuals as the input for noise modelling makes sense, since in fact the residuals are estimates of the noise. 
 
-Note: other methods to "extract" a stationary time series from the original time series, such as differencing or taking a moving average, were discussed as well.
-
-A problem with the noise process of a time series is that often there is time-correlation: in contrast to a white noise signal, the observations with different time lags depend on each other - this is referred to as colored noise. The dependency can be modelled by the autocovariance function ([Chapter 4.5](ACF)). With that, the noise process can be modelled using the Autoregressive Moving Average model ([Chapter 4.6](ARMA)).
+A problem with the noise process of a time series is that often there is auto-correlation: in contrast to a white noise signal, the observations with different time lags depend on each other - this is referred to as colored noise. The dependency can be modelled by the autocovariance function ([Chapter 4.5](ACF)). With that, the noise process can be modelled using the Autoregressive model ([Chapter 4.6](AR)).
 
 Now that we are able to model both signal-of-interest and the noise process, we can start forecasting.
 
@@ -22,16 +20,15 @@ In summary, given a time series $Y=\mathrm{Ax}+\epsilon$, the workflow is as fol
 
 1. Estimate the signal-of-interest $\hat{X}=(\mathrm{A}^T\Sigma_{Y}^{-1}\mathrm{A})^{-1}\mathrm{A}^T\Sigma_{Y}^{-1}Y$.
 
-2. Model the noise using the Autoregressive Moving Average (ARMA) model, using the stationary time series $S:=\hat{\epsilon}=Y-\mathrm{A}\hat{X}$.
+2. Model the noise using the Autoregressive (AR) model, using the stationary time series $S:=\hat{\epsilon}=Y-\mathrm{A}\hat{X}$.
 
 3. Predict the signal-of-interest: $\hat{Y}_{signal}=\mathrm{A}_p\hat{X}$, where $\mathrm{A}_p$ is the design matrix describing the functional relationship between the future values $Y_p$ and $\mathrm{x}$.
 
-4. Predict the noise $\hat{\epsilon}_p$ based on the ARMA model.
+4. Predict the noise $\hat{\epsilon}_p$ based on the AR model. $\hat{\epsilon}_p = \Sigma_{Y_pY}\Sigma_Y^{-1}\hat{\epsilon}$, where $\Sigma_{Y_pY}$ is the covariance matrix between the future values $Y_p$ and the observed values $Y$.
 
 5. Predict future values of the time series: $\hat{Y}_p=\mathrm{A}_p\hat{X}+\hat{\epsilon}_p$.
 
 ```{note}
-Interested readers can find a brief introduction on how to predict signals that are partially deterministic and stochastic using Best Linear Unbiased Prediction (BLUP) in the section on [Supplementary Material](BLUP).
+This procedure is a general approach to forecasting time series data. It resembles the process of stochastic inter- and extrapolation, which is used in many fields of science and engineering.
 ```
 
-
diff --git a/book/time_series/generated_time_series.csv b/book/time_series/generated_time_series.csv
new file mode 100644
index 0000000000000000000000000000000000000000..76309a92cac0a5b402b66281e4c5e57d13a5da9c
--- /dev/null
+++ b/book/time_series/generated_time_series.csv
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diff --git a/book/time_series/intro.md b/book/time_series/intro.md
index dcb1dd0b017debdaaf57eccc54940c2a32275e7a..b761b9242269c847de51122f8554436ab57a1c50 100644
--- a/book/time_series/intro.md
+++ b/book/time_series/intro.md
@@ -2,5 +2,12 @@
 
 In this chapter, we will first introduce the components to describe a time series: trend, signal, offsets, irregularities and noise. It will then be shown how to estimate the signal-of-interest (everything except noise).
 
-Next, we will consider stationary time series, meaning that the statistical properties do not depend on the time when the time series was observed. The stationary time series describe an underlying stochastic process, which can then be modelled, for instance using an Autoregressive Moving Average (ARMA) model. The final goal is to use the time series for estimating the components such as trend and seasonality, as well as to predict future values, for which we do need to take into account the stochastic process.
+Next, we will consider stationary time series, meaning that the statistical properties do not depend on the time when the time series was observed. The stationary time series describe an underlying stochastic process, which can then be modelled, for instance using an Autoregressive (AR) model. The final goal is to use the time series for estimating the components such as trend and seasonality, as well as to predict future values, for which we do need to take into account the stochastic process.
 
+```{figure} ./figs/tsa_cover.png
+:name: cover
+:width: 600px
+:align: center
+
+Recorded and expected global warming from 1960 to 2100, from IPCC report ([Masson-Delmotte, et al. (20219)](https://www.researchgate.net/profile/Peter-Marcotullio/publication/330090901_Sustainable_development_poverty_eradication_and_reducing_inequalities_In_Global_warming_of_15C_An_IPCC_Special_Report/links/6386062b48124c2bc68128da/Sustainable-development-poverty-eradication-and-reducing-inequalities-In-Global-warming-of-15C-An-IPCC-Special-Report.pdf))
+```
diff --git a/book/time_series/intro_graph.ipynb b/book/time_series/intro_graph.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..159f2a26b319d0f0bbea114ae6100b76844960f3
--- /dev/null
+++ b/book/time_series/intro_graph.ipynb
@@ -0,0 +1,387 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Time Series introduction\n",
+    "\n",
+    "The code on this page can be used interactively: click {fa}`rocket` --> {guilabel}`Live Code` in the top right corner, then wait until the message {guilabel}`Python interaction ready!` appears.\n",
+    "\n",
+    "This page creates an interactive plot of a simulated time series. Throughout this section we will discuss these components.\n",
+    "\n",
+    "(components)=\n",
+    "# Components of time series\n",
+    "\n",
+    "A time series is a discrete time sequence of data points indexed in time which can be used to study a phenomenon. It is a record of the data collected at different points in time, it consists of discrete time samples of typically a continuous-time phenomenon in reality.  The data are usually collected at fixed time intervals rather than just recording them intermittently or randomly. The fixed interval $\\Delta t$, in the time domain, is defined as 'sampling interval' and, in the frequency domain, is defined as 'sampling rate' or 'sampling frequency', expressed for example in Hz.\n",
+    "\n",
+    "$$ \\Delta t = \\frac{1}{f_s} $$\n",
+    "\n",
+    "A time series is denoted as \n",
+    "\n",
+    "$$Y(t) = [Y(t_1), Y(t_2), \\ldots{}, Y(t_m)]^T$$\n",
+    "\n",
+    "The $Y(t_i)$ are random variables, since the data is affected by noise.\n",
+    "\n",
+    "The time instants, also defined as epochs, are $t_i = i \\Delta t$, indicating that the samples are equally spaced in time intervals of $\\Delta t$. Assuming a unit time interval (i.e., $\\Delta t=1$), then $t_i = i$ and we can write the time series as \n",
+    "\n",
+    "$$Y(t) = [Y(1), Y(2), \\ldots{}, Y(m)]^T = [Y_1, Y_2, \\ldots{}, Y_m]^T$$\n",
+    "\n",
+    "```{figure} ./figs/time_series.png\n",
+    ":name: time_series\n",
+    ":width: 700px\n",
+    ":align: center\n",
+    "\n",
+    "Example of time series with equally spaced time interval $\\Delta t$\n",
+    "```\n",
+    "\n",
+    "A time series can be decomposed as follows:\n",
+    "\n",
+    "$$Y(t) = tr(t) + s(t) + o(t) + b(t) + \\epsilon(t)$$\n",
+    "\n",
+    "where we distinguish the following components:\n",
+    "\n",
+    "1. $tr(t)$ = trend, provides the general behavior and variation of the process\n",
+    "2. $s(t)$ = seasonality, shows the regular seasonal variations\n",
+    "3. $o(t)$ = offset, is a discontinuity (or jump) in the data\n",
+    "4. $b(t)$ = irregularities and outliers (also referred to as biases), due to unexpected reasons. Irregularities will not be considered in this book.\n",
+    "5. $\\epsilon(t)$ = noise process, can be white or colored noise.\n",
+    "\n",
+    "## Trend\n",
+    "\n",
+    "The trend is the general pattern of the time series and shows its long-term changes. The trend can be linear, however higher order polynomials are also possible.\n",
+    "\n",
+    "```{figure} ./figs/trend.png\n",
+    ":name: trend\n",
+    ":width: 600px\n",
+    ":align: center\n",
+    "\n",
+    "Monthly time series of global mean sea level measurements using Satellite Altimetry technique. Source image: https://www.cmar.csiro.au/sealevel/sl_hist_last_decades.html\n",
+    "```\n",
+    "\n",
+    "{numref}`trend` shows a positive trend (red line) of around $3.5$ mm/year, which in this case indicates sea level rise. This however needs to be further investigated and tested statistically (see {ref}`hypothesis_testing` and also {ref}`modelling_tsa`).\n",
+    "\n",
+    "Trend analysis expresses the changes of the variable of interest with respect to time $t$. Different types of trend are possible and for now we will mainly focus on linear trend, i.e. the time-dependent variable $Y(t)$ changes at a (constant) linear rate over time: $Y_t = y_0 + r t + \\epsilon_t$. Other trends are however also possible, for example, quadratic, which includes $c t^2$, or log linear $\\log(Y_t) = y_0 + r t + \\epsilon_t$.\n",
+    "\n",
+    "\n",
+    "## Seasonality\n",
+    "\n",
+    "Seasonal variations explain regular fluctuations in a certain period of time (e.g. a year), usually caused by climate and weather conditions (e.g. temperature, rainfall), cycles of seasons, customs, traditional habits, weekends, or holidays. For example, the weekly signal is usually evident in the volume of people engaged in shopping (likely more people prefer going shopping in the weekends)\n",
+    "\n",
+    "From {numref}`trend` it is also possible to see the seasonal variations: in fact sea levels are higher in summer and lower in winter. The annual warming/cooling cycle is the main contributor to these seasonal variations.\n",
+    "\n",
+    "Regular seasonal variations in a time series might be handled by using a sinusoidal model with one or more sinusoids with frequency that may be known or unknown depending on the context. In fig {numref}`trend`, cyclical behavior with a period of 1 year can be observed. A harmonic model for seasonal variation can be of the following two equivalent forms (using that $\\cos(u+v)= \\cos u \\cos v - \\sin u \\sin v$):\n",
+    "\n",
+    "$$ \n",
+    "\\begin{align*}\n",
+    "Y(t) &= \\sum_{k=1} ^p A_k  \\cos(k \\omega_0  t + \\theta_k)  + \\epsilon_t\\\\\n",
+    "&= \\sum_{k=1} ^p \\left(a_k  \\cos(k \\omega_0  t) + b_k  \\sin(k \\omega_0 t) \\right)+ \\epsilon_t\n",
+    "\\end{align*}\n",
+    "$$\n",
+    "\n",
+    "With the coefficients $a_k = A_k\\cos\\theta_k$ and $b_k=-A_k\\sin\\theta_k$, and where $\\omega_0$ is the base (fundamental) frequency of the seasonal variation and is fixed or is determined by Spectral Analysis. To be more specific, we can use the {ref}`psd` to determine the unknown frequencies. \n",
+    "\n",
+    "Once $\\omega_ 0$ is set, the coefficients $a_k $ and $b_k$ can be determined using the least-squares method, since the equation is linear in $a_k$ and $b_k$. From this the original sinusoids can be obtained using:\n",
+    "\n",
+    "$$ A_k = \\sqrt{a_k^2 + b_k^2}, \\hspace{1cm} \\theta_k = \\arctan(-\\frac{b_k}{a_k}), \\hspace{1cm} k = 1, \\ldots{}, p $$\n",
+    "\n",
+    "```note\n",
+    "This transformation is necessary to make the seasonal component phase-independent. Using regular estimation methods, we cannot linearly estimate the phase of the sinusoidal function. However by transforming the sinusoidal function into a linear combination of sine and cosine functions, we can estimate the phase of the seasonal component.\n",
+    "```\n",
+    "\n",
+    ":::{card} Worked example - seasonality signal\n",
+    "\n",
+    "Show that the time series \n",
+    "\n",
+    "$$Y(t)=A \\cos(\\omega_0 t + \\theta)$$ \n",
+    "\n",
+    "with given $\\omega_0$, can be rewritten as\n",
+    "\n",
+    "$$Y(t)=a \\cos(\\omega_0 t) + b \\sin(\\omega_0 t)$$\n",
+    "\n",
+    "and derive the formulation of $A$ and $\\theta$.\n",
+    "\n",
+    "Hint: you might need to know trigonometric identity $\\cos(u+v)=\\cos(u)\\cos(v)-\\sin(u)\\sin(v)$\n",
+    "\n",
+    "````{admonition} Solution\n",
+    ":class: tip, dropdown\n",
+    "\n",
+    "Using the trigonometric identity to rewrite:\n",
+    "\n",
+    "$ Y(t)=A \\cos(\\omega_0 t + \\theta) = A (\\cos(\\omega_0 t)\\cos(\\theta)-\\sin(\\omega_0 t)\\sin(\\theta)) $\n",
+    "\n",
+    "Retrieving the functions for a and b\n",
+    "\n",
+    "$ a = A \\cos(\\theta) \\hspace{1cm} b = -A \\sin(\\theta)$\n",
+    "\n",
+    "Squaring both functions in order to get rid of the sin and cos\n",
+    "\n",
+    "$ a^2 = A^2 \\cos^2(\\theta) \\hspace{1cm} b^2 = A^2 \\sin^2(\\theta) $\n",
+    "\n",
+    "Adding both functions together\n",
+    "\n",
+    "$ a^2 + b^2 = A^2 (\\cos^2(\\theta) + \\sin^2(\\theta)) $\n",
+    "\n",
+    "Using this property to simplify:\n",
+    "\n",
+    "$ \\cos^2(\\theta) + \\sin^2(\\theta) = 1 $\n",
+    "\n",
+    "$ a^2 + b^2 = A^2 $\n",
+    "\n",
+    "Take square root to find A\n",
+    "\n",
+    "$ \\sqrt{a^2 + b^2} = A $ \n",
+    "\n",
+    "For $\\theta$ we rewrite the second function\n",
+    "\n",
+    "$ a = A \\cos(\\theta) \\hspace{1cm} -b = A \\sin(\\theta)$\n",
+    "\n",
+    "$ \\frac{-b}{a} = \\frac{\\sin(\\theta)}{\\cos(\\theta)} = \\tan(\\theta) $\n",
+    "\n",
+    "$ \\theta = \\arctan(\\frac{-b}{a}) $\n",
+    "\n",
+    "\n",
+    "[This video](https://youtu.be/8kqQiI4ni68) includes the solution to this exercise. \n",
+    "````\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "## Offset (jump)\n",
+    "\n",
+    "Offsets are sudden changes or shifts in time series. There are different underlying reasons why we encounter offsets in time series. \n",
+    "\n",
+    "```{figure} ./figs/offset.png\n",
+    ":name: offset\n",
+    ":width: 700px\n",
+    ":align: center\n",
+    "\n",
+    "Example of time series with two offsets. \n",
+    "```\n",
+    "\n",
+    "As a deterministic sudden change, offsets can be handled by a step function such as a heaviside step function with an epoch (time instant) that can be known or unknown (to be detected) depending on the time series.\n",
+    "\n",
+    "In this case the time series is written as: \n",
+    "\n",
+    "$$ Y(t) = \\sum_{k=1}^q o_k u_k(t)+\\epsilon_t$$\n",
+    "\n",
+    "where $q$ is the series of offsets (in {numref}`offset` there are two offsets, hence $q=2$) and each of them is expressed as a Heaviside step function \n",
+    "\n",
+    "$$u_k(t) = \\left\\{\n",
+    "\\begin{array}{ll}\n",
+    "      0 & \\text{if} \\hspace{0.3cm} t<t_k \\\\\n",
+    "      1 & \\text{if} \\hspace{0.3cm} t\\geq t_k \\\\\n",
+    "\\end{array} \n",
+    "\\right.  $$\n",
+    "\n",
+    "Once the time instant ($t_k$) of the offset is known, the amplitude can be estimated using least-squares.\n",
+    "\n",
+    "## Noise \n",
+    "\n",
+    "Noise simply refers to random fluctuations in the time series about its typical pattern. In general we can talk about white and colored noise in time series analysis. The following characteristics are associated with noise:\n",
+    "\n",
+    "- Noise is not synonymous with error, although random variation, including measurement errors, contributes to noise. Essentially, noise represents the unpredictable fluctuations in data, while errors encompass any inaccuracies that may arise from a range of factors, including both random variations and systematic issues.\n",
+    "- It is required to filter out unwanted random variations, and detect meaningful information (i.e., a signal) from noise processes.\n",
+    "- Transforming data from the time domain to the frequency domain allows to filter out the frequencies that pollute the data.\n",
+    "- White noise can be decomposed into its constituent components (frequencies).  In principle, white noise contains all wavelengths/colors (like white light), each contributing equally to the fluctuations observed in the data.\n",
+    "- Colored noise can seriously affect the analysis of time series, and their parameters of interest. Short-term colored noise has also predictive property (used for forecasting).\n",
+    "\n",
+    "A purely stationary random process (or white noise process) yields a sequence of uncorrelated zero-mean random variables. This zero-mean random process is of the form\n",
+    "\n",
+    "$$ Y(t)=Y_t=\\epsilon_t $$\n",
+    "\n",
+    "where $\\epsilon_t$ is the independent identically distributed (i.i.d.) error at epoch $t$. Therefore, the observation/noise at time $t$ is not dependent on any of the previous observations $Y_t$.\n",
+    "\n",
+    "### Stochastic model\n",
+    "\n",
+    "A stationary zero-mean random process has an expectation of zero (functional model), and a scaled identity matrix as its covariance matrix (stochastic model). The functional and stochastic models of white noise are of the form \n",
+    "\n",
+    "$$\n",
+    "\\mathbb{E}(Y) =  \\mathbb{E} \\left[\\begin{array}{c} y_1 \\\\ y_2 \\\\ \\vdots \\\\ y_m \\end{array}\\right] = \\left[\\begin{array}{c} 0 \\\\ 0 \\\\ \\vdots \\\\ 0 \\end{array}\\right]\n",
+    "$$\n",
+    "\n",
+    "and \n",
+    "\n",
+    "$$\n",
+    "\\mathbb{D}(Y) =  \\Sigma_{Y} = \\sigma^2 \\left[\\begin{array}{ccc} 1 & 0 & \\ldots{} & 0 \\\\ 0 & 1 & \\ldots{} & 0 \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ 0 & 0 & \\ldots{} & 1 \\end{array}\\right]\n",
+    "$$\n",
+    "\n",
+    "The noise can be represented with, for example, a Gaussian distribution with mean $\\mu=0$ and variance $\\sigma^2$, that is $\\epsilon(t) \\sim \\textbf{N}(0, \\sigma^2)$.\n",
+    "\n",
+    ":::{card} Example - time series consisting of a trend, annual signal (seasonality), an offset and pure random noise (white noise)\n",
+    "\n",
+    "It can be written as \n",
+    "\n",
+    "$$Y(t) = y_0 + rt + a \\text{cos}(\\omega_0 t) + b \\text{sin}(\\omega_0 t) + o u_k(t) + \\epsilon(t)$$\n",
+    "\n",
+    "where \n",
+    "- $y_0$ is the intercept (e.g. in mm)\n",
+    "- $r$ is the rate (e.g. in mm/year)\n",
+    "- $a$ and $b$ are the coefficients of the signal, (e.g. annual signal)\n",
+    "- $\\omega_0$ is the frequency (e.g. 1 cycle/year)\n",
+    "- $o$ is the offset starting at time $t_k$\n",
+    "- $\\epsilon(t)$ is the i.i.d. random Gaussian noise, i.e. $\\epsilon(t) \\sim \\textbf{N}(0, \\sigma^2)$.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": [
+     "auto-execute-page",
+     "thebe-remove-input-init"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import ipywidgets as widgets\n",
+    "from ipywidgets import interact, Layout\n",
+    "import micropip\n",
+    "await micropip.install(\"ipympl\")\n",
+    "\n",
+    "%matplotlib widget\n",
+    "\n",
+    "x = np.linspace(0, 10, 500)\n",
+    "\n",
+    "def gen_seasons(a=0, b=0, frequency=0):\n",
+    "    y = a * np.cos(frequency*x) + b * np.sin(frequency*x)\n",
+    "    return y\n",
+    "\n",
+    "def gen_offset(offset_loc=0, offset_size=0):\n",
+    "    offset = np.zeros_like(x)\n",
+    "    for ind, i in enumerate(x):\n",
+    "        if i >= offset_loc:\n",
+    "            offset[ind] = offset_size\n",
+    "    return offset\n",
+    "\n",
+    "def gen_trend(trend_slope=0):\n",
+    "    y = trend_slope * x\n",
+    "    return y\n",
+    "\n",
+    "def gen_noise(std=0):\n",
+    "    np.random.seed(int(std*100))\n",
+    "    y = np.random.normal(0, std, size=x.shape)\n",
+    "    return y\n",
+    "\n",
+    "def generate_data(trend_slope=0, std=0, a=0, b=0, frequency=0, offset_loc=5, offset_size=0):\n",
+    "    y = gen_trend(trend_slope) + gen_noise(std) + gen_offset(offset_loc, offset_size) + gen_seasons(a, b, frequency)\n",
+    "    return y\n",
+    "\n",
+    "# Function to plot the data\n",
+    "def plot_data(trend_slope=0, a=0, b=0, frequency=1, offset_location=5, offset_size=0, standard_dev=0):\n",
+    "    y = generate_data(trend_slope, standard_dev, a, b, frequency, offset_location, offset_size)\n",
+    "    y_trend = gen_trend(trend_slope)\n",
+    "    y_seas = gen_seasons(a, b, frequency)\n",
+    "    y_offs = gen_offset(offset_location, offset_size)\n",
+    "    y_noise = gen_noise(standard_dev)\n",
+    "\n",
+    "    fig, axs = plt.subplots(5, 1, figsize=(8, 8))\n",
+    "    # plt.figure(figsize=(10, 6))\n",
+    "    axs[0].plot(x, y, label=\"Generated Data\")\n",
+    "\n",
+    "    axs[0].set_title(\"Data with Optional Trend and Noise\")\n",
+    "    axs[0].grid(True)\n",
+    "    axs[0].tick_params(axis='x', labelbottom=False)  # Remove x-tick labels\n",
+    "\n",
+    "    axs[1].plot(x, y_trend, label=\"Trend\")\n",
+    "    axs[1].grid(True)\n",
+    "    axs[1].legend()\n",
+    "    axs[1].tick_params(axis='x', labelbottom=False)  # Remove x-tick labels\n",
+    "    axs[1].set_ylim([-20, 20])\n",
+    "\n",
+    "    # plt.ylabel('Different time series components', loc='bottom')\n",
+    "    axs[2].plot(x, y_seas, label=\"Seasonality\")\n",
+    "    axs[2].grid(True)\n",
+    "    axs[2].legend()\n",
+    "    axs[2].tick_params(axis='x', labelbottom=False)  # Remove x-tick labels\n",
+    "    axs[2].set_ylim([-5.5, 5.5])\n",
+    "\n",
+    "    axs[3].plot(x, y_offs, label=\"Offset\")\n",
+    "    axs[3].grid(True)\n",
+    "    axs[3].legend()\n",
+    "    axs[3].tick_params(axis='x', labelbottom=False)  # Remove x-tick labels\n",
+    "    axs[3].set_ylim([-5.5, 5.5])\n",
+    "\n",
+    "    axs[4].plot(x, y_noise, label=\"Noise\")\n",
+    "    axs[4].grid(True)\n",
+    "    axs[4].legend()\n",
+    "    axs[4].set_ylim([-3, 3])\n",
+    "\n",
+    "    # plt.tight_layout()\n",
+    "    plt.xlabel('Time')\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": [
+     "auto-execute-page",
+     "thebe-remove-input-init"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5c7b334f4fa45b98636bd8dd1785a4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=1.0, description='Trend Slope', layout=Layout(width='40%'), max=2.0, m…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "    \n",
+    "# Creating interactive widgets\n",
+    "style = {'description_width': 'initial'}\n",
+    "interact(plot_data,\n",
+    "         trend_slope=widgets.FloatSlider(value=1, min=-2.0, max=2.0, step=0.05, description=\"Trend Slope\", style=style, layout=Layout(width='40%')),\n",
+    "         a=widgets.FloatSlider(value=0, min=0, max=5.0, step=0.05,description=\"a\", style=style, layout=Layout(width='40%')),\n",
+    "         b=widgets.FloatSlider(value=2, min=0, max=5.0, step=0.05,description=\"b\", style=style, layout=Layout(width='40%')),\n",
+    "         frequency=widgets.FloatSlider(value=4, min=0, max=10.0, step=0.05,description=\"frequency\", style=style, layout=Layout(width='40%')),\n",
+    "         offset_location=widgets.FloatSlider(value=5, min=0, max=10, step=0.1, description=\"offset location\", style=style, layout=Layout(width='40%')),\n",
+    "         offset_size=widgets.FloatSlider(value=5, min=-10, max=10, step=0.1, description=\"offset size\", style=style, layout=Layout(width='40%')),\n",
+    "         standard_dev=widgets.FloatSlider(value=0.5, min=0, max=2, step=0.005,description=\"Standard deviation\", style=style, layout=Layout(width='40%')));\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "TAMude",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/book/time_series/modelling.md b/book/time_series/modelling.md
index bcb4bca4ef774e7dd3f7043c080e878814125a33..28868129e0cca2dffd07739aa13b4f19bda67287 100644
--- a/book/time_series/modelling.md
+++ b/book/time_series/modelling.md
@@ -4,23 +4,20 @@
 The goal is now to:
 
 * estimate parameters of interest (i.e., components of time series) using **Best Linear Unbiased Estimation (BLUE)**;
-* evaluate the confidence intervals of parameters of interest;
-* identify an appropriate model using **hypothesis testing**.
-
-$$\mathcal{H}_0: Y=\mathrm{Ax}+\epsilon \hspace{5px}\text{vs.}\hspace{5px} \mathcal{H}_a: Y=\mathrm{Ax+C}\nabla+\epsilon$$
+* evaluate the confidence intervals of the estimators for the parameters of interest;
 
 ## Components of time series
 
 As already discussed, we will distinguish the following components in a time series:
 
 * **Trend:** General behavior and variation of the process. This often is a linear trend with an unknown intercept $y_0$ and a rate $r$.
-* **Seasonality:** Regular seasonal variations, which can be expressed as sine functions with (un)known frequency $\omega$, and unknown amplitude $A$ and phase $\theta$, or with unknowns $a(=A\sin\theta)$ and $b(=A\cos\theta)$, see [example](season).
+* **Seasonality:** Regular seasonal variations, which can be expressed as sine functions with (un)known angular frequency $\omega_0$, and unknown amplitude $A$ and phase $\theta$.
 * **Offset:** A jump of size $o$ in a time series starting at epoch $t_k$.
-* **Noise:** White or colored noise (e.g., ARMA process).
+* **Noise:** White or colored noise (e.g., AR process).
 
 ## Best Linear Unbiased Estimation (BLUE)
 
-If the components of time series are known, we may use the linear model of observation equations to estimate those components.
+If the components of time series are known, we may use the linear model of observation equations to estimate those components. We will use the theory from the chapter on [observation theory](BLUE) to estimate the parameters of interest.
 
 Consider the linear model of observation equations as
 
@@ -30,11 +27,11 @@ Recall that the BLUE of $\mathrm{x}$ is:
 
 $$\hat{X}=(\mathrm{A}^T\Sigma_{Y}^{-1}\mathrm{A})^{-1}\mathrm{A}^T\Sigma_{Y}^{-1}Y,\hspace{10px}\Sigma_{\hat{X}}=(\mathrm{A}^T\Sigma_{Y}^{-1}\mathrm{A})^{-1}$$
 
-The BLUE of $Y$ and $\epsilon$ is
+The BLUE of $Y$  is:
 
 $$\hat{Y}=\mathrm{A}\hat{X},\hspace{10px}\Sigma_{\hat{Y}}=\mathrm{A}\Sigma_{\hat{X}}\mathrm{A}^T$$
 
-and
+and $\epsilon$ is:
 
 $$\hat{\epsilon}=Y-\hat{Y},\hspace{10px}\Sigma_{\hat{\epsilon}}=\Sigma_{Y}-\Sigma_{\hat{Y}}$$
 
@@ -42,16 +39,16 @@ $$\hat{\epsilon}=Y-\hat{Y},\hspace{10px}\Sigma_{\hat{\epsilon}}=\Sigma_{Y}-\Sigm
 
 The linear model, consisting of the above three components plus noise, is of the form
 
-$$Y_t = y_0+rt+a\cos{\omega t}+b\sin{\omega t}+ou_k(t)+\epsilon_t$$
+$$Y_t = y_0+rt+a\cos{\omega_0 t}+b\sin{\omega_0 t}+ou_k(t)+\epsilon_t$$
 
 The linear model should indeed be written for all time instances $t_1,...,t_m$, resulting in $m$ equations as:
 
 $$
 \begin{align*}
-Y(t_1) &= y_0+rt_1+a\cos{\omega t_1}+b\sin{\omega t_1}+ou_k(t_1)+\epsilon(t_1)\\ 
-Y(t_2) &= y_0+rt_2+a\cos{\omega t_2}+b\sin{\omega t_2}+ou_k(t_2)+\epsilon(t_2)\\ 
+Y(t_1) &= y_0+rt_1+a\cos{\omega_0 t_1}+b\sin{\omega_0 t_1}+ou_k(t_1)+\epsilon(t_1)\\ 
+Y(t_2) &= y_0+rt_2+a\cos{\omega_0 t_2}+b\sin{\omega_0 t_2}+ou_k(t_2)+\epsilon(t_2)\\ 
 &\vdots \\ 
-Y(t_k) &= y_0+rt_k+a\cos{\omega t_k}+b\sin{\omega t_k}+ou_k(t_k)+\epsilon(t_k)\\ &\vdots \\ Y(t_m) &= y_0+rt_m+a\cos{\omega t_m}+b\sin{\omega t_m}+ou_k(t_m)+\epsilon(t_m)
+Y(t_k) &= y_0+rt_k+a\cos{\omega_0 t_k}+b\sin{\omega_0 t_k}+ou_k(t_k)+\epsilon(t_k)\\ &\vdots \\ Y(t_m) &= y_0+rt_m+a\cos{\omega_0 t_m}+b\sin{\omega_0 t_m}+ou_k(t_m)+\epsilon(t_m)
 \end{align*}
 $$
 
@@ -66,12 +63,12 @@ $$
 Y_1\\ \vdots\\ Y_{k-1}\\  Y_k\\ \vdots\\ 
 Y_m\end{bmatrix}}^{Y} = 
 \overbrace{\begin{bmatrix}
-1&t_1&\cos{\omega t_1}&\sin{\omega t_1}&0
+1&t_1&\cos{\omega_0 t_1}&\sin{\omega_0 t_1}&0
 \\  \vdots&\vdots&\vdots&\vdots&\vdots\\ 
-1&t_{k-1}&\cos{\omega t_{k-1}}&\sin{\omega t_{k-1}}&0\\ 
-1&t_k&\cos{\omega t_k}&\sin{\omega t_k}&1\\ 
+1&t_{k-1}&\cos{\omega_0 t_{k-1}}&\sin{\omega_0 t_{k-1}}&0\\ 
+1&t_k&\cos{\omega_0 t_k}&\sin{\omega_0 t_k}&1\\ 
 \vdots&\vdots&\vdots&\vdots&\vdots\\ 
-1&t_m&\cos{\omega t_m}&\sin{\omega t_m}&1\end{bmatrix}}^{\mathrm{A}}\overbrace{\begin{bmatrix}y_0\\ r\\ a\\ b\\ o\end{bmatrix}}^{\mathrm{x}}+\overbrace{\begin{bmatrix}\epsilon_1\\ \vdots\\ \epsilon_{k-1} \\ \epsilon_k\\ \vdots\\ \epsilon_m\end{bmatrix}}^{\epsilon}$$
+1&t_m&\cos{\omega_0 t_m}&\sin{\omega_0 t_m}&1\end{bmatrix}}^{\mathrm{A}}\overbrace{\begin{bmatrix}y_0\\ r\\ a\\ b\\ o\end{bmatrix}}^{\mathrm{x}}+\overbrace{\begin{bmatrix}\epsilon_1\\ \vdots\\ \epsilon_{k-1} \\ \epsilon_k\\ \vdots\\ \epsilon_m\end{bmatrix}}^{\epsilon}$$
 
 with the $m\times m$ covariance matrix
 %MMMMM should we keep sigma for the diagonal and c_i for the non-diagonal elements?
@@ -80,7 +77,7 @@ $$\Sigma_{Y}=\begin{bmatrix}\sigma_1^2&\sigma_{12}&\dots&\sigma_{1m}\\ \sigma_{2
 
 :::{card} Exercise
 
-A time series exhibits a linear regression model $Y(t)=y_0 + rt + \epsilon(t)$. The measurements have also been taken at a measurement frequency of 10 Hz, producing epochs of $t=0.1,0.2, \dots,100$ seconds, so $m=1000$. Later an offset was also detected at epoch 260 using statistical hypothesis testing. For the linear model $Y=\mathrm{Ax}+\epsilon$, establish an approprate design matrix that can capture all the above effects.
+A time series exhibits a linear regression model $Y(t)=y_0 + rt + \epsilon(t)$. The measurements have also been taken at a measurement frequency of 10 Hz, producing epochs of $t=0.1,0.2, \dots,100$ seconds, so $m=1000$. Later an offset was also detected at epoch 260 using statistical hypothesis testing. For the linear model $Y=\mathrm{Ax}+\epsilon$, establish an appropriate design matrix that can capture all the above effects.
 
 ```{admonition} Solution
 :class: tip, dropdown
@@ -140,21 +137,9 @@ where $\sigma_{\hat{r}} = \sqrt{(\Sigma_{\hat{X}})_{22}}$ is the standard deviat
 
 ## Model identification
 
-The design matrix $\mathrm{A}$ is usually assumed to be known. So far, we have assumed the frequency $\omega$ of the periodic pattern (seasonality, for example) in a $a\cos{\omega t} + b\sin{\omega t}$ is known, so the design matrix $\mathrm{A}$ can be directly obtained. In some applications, however, such information is hidden in the data, and needs to be identified/detected. Linear model identification is a way to reach this goal.
-
-***How to determine $\omega$ if it is unknown a priori?***
-
-### Discrete Fourier Transform (DFT)
-
-The first method we will study is the **Discrete Fourier Transform**. The DFT or fast FT (FFT) of a real time series, $Y_t$, is a complex array as
-
-$$\text{DFT}(Y(t))=Y_s(\omega)$$
-
-having a real and an imaginary part. The power at each frequency component can be computed by squaring the magnitude of that frequency component: **power spectral density** (PSD).
-
-$$S_{Y}(\omega)=P_{\omega}=\frac{1}{m\Delta t}|Y_s(\omega)|^2$$
+The design matrix $\mathrm{A}$ is usually assumed to be known. So far, we have assumed the frequency $\omega_0$ of the periodic pattern (seasonality, for example) in a $a\cos{\omega_0 t} + b\sin{\omega_0 t}$ is known, so the design matrix $\mathrm{A}$ can be directly obtained. In some applications, however, such information is hidden in the data, and needs to be identified/detected. Linear model identification is a way to reach this goal.
 
-where $|Y(\omega)|$ is the magnitude at the frequency $\omega$. If a significant seasonality is present at frequency $\omega$, there should be a clear peak at this frequency, so that $S_{Y}(\omega)$ is more peaked than the neighboring powers.
+***How to determine $\omega_0$ if it is unknown a priori?***
 
 #### Example power spectral density
 
@@ -167,90 +152,6 @@ where $|Y(\omega)|$ is the magnitude at the frequency $\omega$. If a significant
 
 Left: time series (grey) and estimated linear trend and sine wave with period of 100. Right: estimated PSD.
 ```
+This means we can estimate the frequency $\omega_0$ of the periodic pattern using the techniques discussed in the chapter on signal processing. Once we have the frequency, we can construct the design matrix $\mathrm{A}$. 
 
-(LS-HE)=
-### Least-Squares Harmonic Estimation (LS-HE)
-
-The second method we will study is BLUE in combination with hypothesis testing, here called **Least Squares Harmonic Estimation** (LS-HE). We make use of the hypothesis testing to test the validity of the linear model and, hence, to improve it.
-
-We put forward two hypotheses:
-
-$$\mathcal{H}_0: Y=\mathrm{Ax}+\epsilon \hspace{5px}\text{vs.}\hspace{5px} \mathcal{H}_a: Y=\mathrm{Ax}+\mathrm{C}\nabla+\epsilon$$
-
-The null hypothesis could be a model without a seasonal component, while the alternative hypothesis would include a seasonal component with a certain choice for $\omega$.
-
-:::{card} **Example**
-
-$$
-\begin{align*}
-\mathcal{H}_0: &Y_t=y_0+rt+\epsilon_t \\
-\mathcal{H}_a: &Y_t=y_0+rt+a\cos{\omega t}+b\sin{\omega t}+\epsilon_t
-\end{align*}
-$$
-
-$$\begin{align*}
-\mathcal{H}_0: &\begin{bmatrix}Y_1\\ Y_2\\ \vdots\\ Y_m\end{bmatrix} = \begin{bmatrix}1&t_1\\ 1&t_2\\ \vdots&\vdots\\ 1&t_m\end{bmatrix}\begin{bmatrix}y_0\\ r\end{bmatrix} + \begin{bmatrix}\epsilon_1\\ \epsilon_2\\ \vdots\\ \epsilon_m\end{bmatrix} \\
-\mathcal{H}_a: &\begin{bmatrix}Y_1\\ Y_2\\ \vdots\\ Y_m\end{bmatrix} = \begin{bmatrix}1&t_1\\ 1&t_2\\ \vdots&\vdots\\ 1&t_m\end{bmatrix}\begin{bmatrix}y_0\\ r\end{bmatrix}+\begin{bmatrix}\cos{\omega t_1}&\sin{\omega t_1}\\ \cos{\omega t_2}&\sin{\omega t_2}\\ \vdots&\vdots\\ \cos{\omega t_m}&\sin{\omega t_m}\end{bmatrix}\begin{bmatrix}a\\ b\end{bmatrix}+\begin{bmatrix}\epsilon_1\\ \epsilon_2\\ \vdots\\ \epsilon_m\end{bmatrix}
-\end{align*}$$
-
-![hypotheses](./figs/hypotheses.png "hypotheses")
-:::
-
-The [Generalized Likelihood Ratio Test](GLRT) statistic is given by
-
-$$\begin{align*}
-T_q &= \hat{\epsilon}^T\Sigma_Y^{-1}\hat{\epsilon}-\hat{\epsilon}_a^T\Sigma_Y^{-1}\hat{\epsilon}_a \\ &=\hat{\epsilon}^T\Sigma_{Y}^{-1}\mathrm{C}(\mathrm{C}^T\Sigma_{Y}^{-1}\Sigma_{\hat{\epsilon}}\Sigma_{Y}^{-1}\mathrm{C})^{-1}\mathrm{C}^T\Sigma_{Y}^{-1}\hat{\epsilon}
-\end{align*}$$
-
-where $\hat{\epsilon}$ and $\hat{\epsilon}_a$ refer to the BLUE residuals obtained with the null and alternative hypothesis, respectively. 
-
-The derivation of the second equality is beyond the scope of this book, but the advantage of this expression is that it only requires to apply BLUE with the model of the null hypothesis; the alternative model is accounted for with matrix $\mathrm{C}$.
-
-This test statistic, having a central $\chi^2$-square distribution under $\mathcal{H}_0$, can be tested for a given confidence level: 
-
-$$T_q\sim\chi^2(q,0)$$
-
-In our example above, we have that $q=2$, the number of extra parameters in $\nabla=[a,b]^T$.
-
-**Special case:** for a zero-mean time series and white noise time series with $\Sigma_{Y}=\sigma^2 I$ we have
-
-$$Y=\hat{\epsilon} \quad \Rightarrow \mathbb{E}(Y)=0 \quad \Rightarrow \mathrm{A}=0$$ 
-
-In this case the test statistic simplifies to:
-
-$$T_q = \frac{1}{\sigma^2}Y^T \mathrm{C}(\mathrm{C}^T\mathrm{C})^{-1}\mathrm{C}^TY$$
-
-:::{card} **Proof**
-
-```{admonition} MUDE exam information
-:class: tip, dropdown
-This proof is optional and will not be assessed on the exam.
-```
-
-If we assume $\Sigma_{Y}=\sigma^2I$ and $Y=\hat{\epsilon}$ such that $\mathrm{A}=0$, we have
-
-$$
-\begin{align*}
-\Sigma_{\hat{\epsilon}}&=\Sigma_{Y}-\Sigma_{\hat{Y}}\\
-& = \sigma^2I - \mathrm{A}(\mathrm{A}^T(\sigma^{-2}I)\mathrm{A})^{-1}\mathrm{A}^T \\
-&=\sigma^2(I - \mathrm{A}(\mathrm{A}^T\mathrm{A})^{-1}\mathrm{A}^T )\\
-& = \sigma^2I
-\end{align*}
-$$
-
-and
-
-$$
-\begin{align*}
-T_q&=\hat{\epsilon}^T\Sigma_{Y}^{-1}\mathrm{C}(\mathrm{C}^T\Sigma_{Y}^{-1}\Sigma_{\hat{\epsilon}}\Sigma_{Y}^{-1}\mathrm{C})^{-1}\mathrm{C}^T\Sigma_{Y}^{-1}\hat{\epsilon}\\
-&= \frac{1}{\sigma^2}Y^T \mathrm{C}(\mathrm{C}^T\mathrm{C})^{-1}\mathrm{C}^TY
-\end{align*}
-$$
-:::
-
-This, in fact, can be shown to be identical to a scaled version (by a factor 2) of the PSD.
-
-```{admonition} Optional: proof of equality of PSD and LS-HE
-:class: tip, dropdown
-[On the equality of the PSD and the LS-HE T-test statistics](./proof.pdf)
-```
\ No newline at end of file
+It is also possible to infer the frequency of the periodic pattern by reasoning. For example, if we know our model depends on temperature, we can assume that the frequency of the seasonal pattern is related to the temperature cycle (e.g., 24 hours). However, this is a more qualitative approach and should be used with caution. Best practice is to use the DFT or PSD to estimate the frequency.
diff --git a/book/time_series/noise.ipynb b/book/time_series/noise.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d8296c5168d440e1690af338017431af974c8bd8
--- /dev/null
+++ b/book/time_series/noise.ipynb
@@ -0,0 +1,228 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(noiseandstoch)=\n",
+    "# Noise and stochastic model\n",
+    "The code on this page can be used interactively: click {fa}`rocket` --> {guilabel}`Live Code` in the top right corner, then wait until the message {guilabel}`Python interaction ready!` appears.\n",
+    "\n",
+    "In the previous section you have learned about the different components that can be present in a time series. Removing all these component, i.e. the functional model, we will be left with the residual term $\\epsilon(t)$. In this section we will take a closer look at the difference between signal and noise, and introduce other types of noise than the traditional white noise.\n",
+    "\n",
+    "## Additional concepts\n",
+    "\n",
+    "In [Signal Processing](SP) the data is just considered to be the signal of interest, whereas here we assume the data is \"contaminated\" with noise, i.e.\n",
+    "\n",
+    "$$Y = \\text{signal} + \\text{noise} $$\n",
+    "\n",
+    "Time series analysis means understanding patterns and, hence, extracting the **signal of interest** from the noisy data.\n",
+    "\n",
+    "### Signal and noise\n",
+    "\n",
+    "How can we describe both signal and noise?\n",
+    "\n",
+    "* **Signal** - the meaningful information that we want to detect: deterministic characteristics by means of mathematical expressions to capture for example trend, seasonality and offsets.\n",
+    "\n",
+    "* **Noise** - random and undesired fluctuation that interferes with the signal: stochastic process are needed to describe this. Parts of the time-correlated noise  needs to be accounted for in predictions, see later {ref}`AR`. \n",
+    "\n",
+    "The example in {numref}`signal_noise` shows that the *signal* can be described by $\\cos(2\\pi t f) + \\sin(2\\pi t f)$. The stochastic model (assuming independent normally distributed observations) would be a scaled identity matrix with variance equal to 1 (middle panel) and 9 (bottom panel), respectively. The signal of interest has been entirely hidden in the background noise in the bottom panel. Techniques from signal processing can be used to detect the frequency.\n",
+    "\n",
+    "```{figure} ./figs/signal_noise.png\n",
+    ":name: signal_noise\n",
+    ":width: 600px\n",
+    ":align: center\n",
+    "\n",
+    "Example of a time series (top graph) affected by noise with different strengths (middle and bottom figures). Note the different scales on the vertical axes.\n",
+    "```\n",
+    "\n",
+    "#### Signal to noise ratio\n",
+    "In signal processing the signal to noise ratio is commonly used to report on the amount of noise present in the model. If we analyze the model $Y = signal + noise$, then Y is a random variable with $E[Y] = E[signal] = \\mu$, and its variance $D(Y) = D(noise) = \\sigma^2$. Using this the signal to noise ratio is often defined as:\n",
+    "\n",
+    "$$ SNR = \\frac{\\mu}{\\sigma}$$\n",
+    "<!-- or alternatively as:\n",
+    "\n",
+    "$$ SNR = \\frac{\\mu^2}{\\sigma^2}$$ -->\n",
+    "\n",
+    "The signal to noise ratio is a measure of how much the signal stands out from the noise. The higher the signal to noise ratio, the more the signal stands out from the noise. Better equipment or more data can increase the signal to noise ratio.\n",
+    "\n",
+    "## Different types of noise\n",
+    "In the ideal case, when the signal is removed, you are left with white noise. A white noise stochastic model has the following properties:\n",
+    "\n",
+    "$$\n",
+    "\\mathbb{E}(Y) =  \\mathbb{E} \\left[\\begin{array}{c} y_1 \\\\ y_2 \\\\ \\vdots \\\\ y_m \\end{array}\\right] = \\left[\\begin{array}{c} 0 \\\\ 0 \\\\ \\vdots \\\\ 0 \\end{array}\\right]\n",
+    "$$\n",
+    "\n",
+    "and \n",
+    "\n",
+    "$$\n",
+    "\\mathbb{D}(Y) =  \\Sigma_{Y} = \\sigma^2 \\left[\\begin{array}{ccc} 1 & 0 & \\ldots{} & 0 \\\\ 0 & 1 & \\ldots{} & 0 \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ 0 & 0 & \\ldots{} & 1 \\end{array}\\right]\n",
+    "$$\n",
+    "Most notable, all observations are uncorrelated (off-diagonal elements of the covariance matrix are equal to 0). When we compute the PSD, the resulting density will be flat over the entire range of frequencies. In other words, a white noise process has equal energy over all frequencies, just like white light. We will show this in the interactive plot at the bottom of this page.\n",
+    "\n",
+    "### Colored noise\n",
+    "In time series it is not guarantied that the individual observations are uncorrelated. At the bottom of this page you will find an interactive plot. You can select four different types of noise: white, pink, red and blue. The noise processes are plotted in combination with the PSD. The [PSD](../signal/spectral_est.md#power-spectral-density-psd) is a measure of the power of the signal at different frequencies. The white noise process has a flat PSD, while the other noise processes have a different shape. The pink noise process has a PSD that decreases with frequency, the red noise process has a PSD that decreases quadratically  with frequency, and the blue noise process has a PSD that increases with frequency. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "auto-execute-page",
+     "thebe-remove-input-init"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "## create a white noise signal and plot it\n",
+    "import numpy as np  \n",
+    "import matplotlib.pyplot as plt\n",
+    "import ipywidgets as widgets\n",
+    "\n",
+    "# create a white noise signal\n",
+    "np.random.seed(0)\n",
+    "N = 1000\n",
+    "x = np.random.randn(N)\n",
+    "\n",
+    "# Function to generate pink noise\n",
+    "def pink_noise(N):\n",
+    "    uneven = N % 2\n",
+    "    X = np.random.randn(N//2+1+uneven) + 1j * np.random.randn(N//2+1+uneven)\n",
+    "    S = np.sqrt(np.arange(len(X)) + 1.)  # +1 to avoid divide by zero\n",
+    "    y = (np.fft.irfft(X/S)).real\n",
+    "    if uneven:\n",
+    "        y = y[:-1]\n",
+    "    return y\n",
+    "\n",
+    "# Function to generate red (brown) noise\n",
+    "def red_noise(N):\n",
+    "    return np.cumsum(np.random.randn(N))\n",
+    "\n",
+    "# Function to generate blue noise\n",
+    "def blue_noise(N):\n",
+    "    uneven = N % 2\n",
+    "    X = np.random.randn(N//2+1+uneven) + 1j * np.random.randn(N//2+1+uneven)\n",
+    "    S = np.sqrt(np.arange(len(X)))  # no +1 here\n",
+    "    y = (np.fft.irfft(X*S)).real\n",
+    "    if uneven:\n",
+    "        y = y[:-1]\n",
+    "    return y\n",
+    "# BEGIN: white_noise function\n",
+    "def white_noise(N):\n",
+    "    return np.random.randn(N)\n",
+    "\n",
+    "\n",
+    "# Generate different noise signals\n",
+    "pink = pink_noise(N)\n",
+    "red = red_noise(N)\n",
+    "blue = blue_noise(N)\n",
+    "x = white_noise(N)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": [
+     "thebe-remove-input-init",
+     "auto-execute-page"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd895686074e4c40b06433752c35d50d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='Noise Type:', index=3, options=('Pink Noise', 'Red Noise', 'Blue N…"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "noise_options = ['Pink Noise', 'Red Noise', 'Blue Noise', 'White Noise']\n",
+    "\n",
+    "# Create a dropdown menu for noise types\n",
+    "dropdown = widgets.Dropdown(\n",
+    "    options=noise_options,\n",
+    "    value='White Noise',\n",
+    "    description='Noise Type:',\n",
+    ")\n",
+    "\n",
+    "# Function to update the plot based on selected noise type\n",
+    "def update_plot_dropdown(noise_type):\n",
+    "\n",
+    "    plt.figure(figsize=(12, 4))\n",
+    "    plt.subplot(2, 1, 1)\n",
+    "    \n",
+    "    if noise_type == 'Pink Noise':\n",
+    "        plt.plot(pink, label='Pink Noise')\n",
+    "    elif noise_type == 'Red Noise':\n",
+    "        plt.plot(red, label='Red Noise')\n",
+    "    elif noise_type == 'Blue Noise':\n",
+    "        plt.plot(blue, label='Blue Noise')\n",
+    "    elif noise_type == 'White Noise':\n",
+    "        plt.plot(x, label='White Noise')\n",
+    "    \n",
+    "    plt.title(f'{noise_type} Signal')\n",
+    "    plt.xlabel('Time Index')\n",
+    "    plt.ylabel('Amplitude')\n",
+    "    plt.legend()\n",
+    "    plt.grid()\n",
+    "    \n",
+    "    plt.subplot(2, 1, 2)\n",
+    "    if noise_type == 'Pink Noise':\n",
+    "        plt.psd(pink, NFFT=2048, Fs=1, color='r', label='Pink Noise')\n",
+    "    elif noise_type == 'Red Noise':\n",
+    "        plt.psd(red, NFFT=2048, Fs=1, color='r', label='Red Noise')\n",
+    "    elif noise_type == 'Blue Noise':\n",
+    "        plt.psd(blue, NFFT=2048, Fs=1, color='r', label='Blue Noise')\n",
+    "    elif noise_type == 'White Noise':\n",
+    "        plt.psd(x, NFFT=2048, Fs=1, color='r', label='White Noise')\n",
+    "\n",
+    "    # plt.yscale('log')\n",
+    "    plt.show()\n",
+    "\n",
+    "widgets.interactive(update_plot_dropdown, noise_type=dropdown)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "If you are interested, you can read more about the different types of noise in the [Wikipedia article](https://en.wikipedia.org/wiki/Colors_of_noise). In here you can also listen to the different types of noise, which might give you a better understanding of the differences.\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "TAMude",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/book/time_series/notebook.ipynb b/book/time_series/notebook.ipynb
deleted file mode 100644
index 69fdcf74aaef940830dd4694761e4e7655f89747..0000000000000000000000000000000000000000
--- a/book/time_series/notebook.ipynb
+++ /dev/null
@@ -1,800 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "144c3547",
-   "metadata": {},
-   "source": [
-    "# TSA Notebook\n",
-    "\n",
-    "MMMMM from 231129 ipynb from Alireza: move these to the right page, or keep all here? If on other page, put under a second-level heading, as above\n",
-    "\n",
-    "With this notebook, you can practice the lectures/videos material. First, you need to study the lecture slides and/or watch the pre-recorded videos. Having studied the lecture material, you are ready to implement your knowledge and practice by doing this notebook.\n",
-    "Enjoy Time Series Analysis!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "6f142ea1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: statsmodels in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (0.12.2)\n",
-      "Requirement already satisfied: numpy>=1.15 in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (from statsmodels) (1.20.3)\n",
-      "Requirement already satisfied: scipy>=1.1 in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (from statsmodels) (1.7.1)\n",
-      "Requirement already satisfied: pandas>=0.21 in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (from statsmodels) (1.3.4)\n",
-      "Requirement already satisfied: patsy>=0.5 in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (from statsmodels) (0.5.2)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (from pandas>=0.21->statsmodels) (2.8.2)\n",
-      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (from pandas>=0.21->statsmodels) (2021.3)\n",
-      "Requirement already satisfied: six in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (from patsy>=0.5->statsmodels) (1.16.0)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import the necessary libraries:\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "!pip install statsmodels\n",
-    "#from statsmodels.tsa.stattools import adfuller\n",
-    "import scipy.signal\n",
-    "from statsmodels.graphics.tsaplots import plot_acf\n",
-    "from scipy import signal"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "374be44f",
-   "metadata": {},
-   "source": [
-    "### Exercise 1. Components of time series  (Video 1) \n",
-    "\n",
-    "**Introduction:** The four components of time series are the trend, seasonality, offset, and noise (white/colored). We use simulated data to show these components here. The observation equation of time series should have the following mathematical representation:\n",
-    "$$Y(t) = y_0 + r t + a \\cos(\\omega_ot) + b\\sin(\\omega_ot) + o {u_k(t)} + \\epsilon(t)= y_0 + r t + A_m \\sin(\\omega_o t+\\phi_0) + o {u_k(t)} + \\epsilon(t)$$\n",
-    "where\n",
-    "- $y_0 $: intercept (e.g. in mm)\n",
-    "- $r$: is the rate (e.g. in mm/day)\n",
-    "- $a$ and $b$ are the coefficients of the periodic signal \n",
-    "- $\\omega$ is the frequency of signal (e.g. cycle/ day)\n",
-    "- $o$ is the size of the offset at time instant $t_k$\n",
-    "- $\\epsilon(t)$ is the random noise with a given variance which follows a Normal distribution: $ \\epsilon(t) \\sim \\textbf{N}(0, \\sigma^2)$\n",
-    "\n",
-    "Here, we are assuming only a single seasonality and offset component. However, in many practical scenarios, there could be multiple components related to these.\n",
-    "\n",
-    "**Exercise:**\n",
-    "You can simulate your time series based on the priori information provided in the scripts. Plot your results and change the input variables to see the effect.\n",
-    "\n",
-    "*The noise follows a normal distribution: use np.random.normal in order to draw random samples from a normal (Gaussian) distribution. Study more for this function here: [normal distribution in python](https://numpy.org/doc/stable/reference/random/generated/numpy.random.normal.html)*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "13f99024",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "np.random.seed(0)  # For reproducibility\n",
-    "\n",
-    "# create your code here:\n",
-    "# create an array with 501 timesteps (in terms of day, so day 0, day 1,...,day 500)\n",
-    "time = np.arange(501) \n",
-    "# m is the length of tim\\epsilon_steps (so number of time series observations)\n",
-    "m = len(time)\n",
-    "# give an intercept of 1 mm\n",
-    "y_0 = 1 \n",
-    "# provide a particular rate of 0.02 mm/day\n",
-    "r = 0.02 \n",
-    "# make the time series observations (so far without noise) based on the above two components:\n",
-    "y1 = y_0 + r*time \n",
-    "\n",
-    "# plot y1 versus time:\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, y1, color='red')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time (day)')\n",
-    "plt.title('$$Y$(t) = 1 + 0.02 t $')\n",
-    "\n",
-    "# introduce (add) a seasonality to the generated data\n",
-    "# A sine signal Am*sin(omega * time + phi_0) can be added\n",
-    "# omega=2*pi*f can be obtained from f = 0.01 cycle/day (1 cycle per 100 days)\n",
-    "omega = 2 * np.pi/100 \n",
-    "# the amplitude of signal is assumed to be 1 mm \n",
-    "Am = 1 \n",
-    "# the initial phase is assumed to be 0.2Ï€ (radian) \n",
-    "phi_0 = 0.2*np.pi # initial phase\n",
-    "# add the sesonality to y1 to make y2\n",
-    "y2 = y1 + Am*np.sin(omega * time + phi_0) \n",
-    "\n",
-    "# plot y2 versus time:\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, y2, color='blue')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time (day)')\n",
-    "plt.title('$$Y$(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€) $')\n",
-    "\n",
-    "# add an offset to y2 at epoch (time instance) 300\n",
-    "t_k = 300 \n",
-    "# offset size of your choice (for example 5 mm) - the jump in your data!\n",
-    "O_k = 5 \n",
-    "y3 = y2.copy() \n",
-    "y3[t_k:] = y3[t_k:] + O_k\n",
-    "# plot y3 versus time to see the effect of the offset:\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, y3, color='g')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time')\n",
-    "plt.title('$$Y$(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€) + 5 u_{300}(t)$')\n",
-    "\n",
-    "# add randon error (white noise) which follows a normal distribution \n",
-    "# (mean of zero mm, and standard deviations of 0.5 mm)\n",
-    "# change these parameters to see the effect\n",
-    "mean = 0 \n",
-    "sigma = 0.5 \n",
-    "et = np.random.normal(loc = mean, scale = sigma, size = m) \n",
-    "y4 = y3 + et \n",
-    "\n",
-    "# plotting:\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, y4, color='red')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time')\n",
-    "plt.title('$$Y$(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€) + 5 u_{300}(t) + N(0,0.5^2)$')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1a918b4",
-   "metadata": {},
-   "source": [
-    "### Exercise 2. Stationary time series (Video 2)\n",
-    "**Introduction:** In the previous exercise, we created and plotted the $Y_4$ time series, now we check its stationarity. Remember that we need to ensure *stationarity* of the time series data-set for *forecasting and predictive models*. \n",
-    "In this excercise, you can test the stationarity of the time series using transformation and visual inspection and the Augmented Dickey-Fuller (ADF) test (The ADF test is optional). \n",
-    "\n",
-    "**Background knowledge:** The ADF test can be performed by using two hypotheses (Null Hypothesis and Alternative Hypothesis):\n",
-    "\n",
-    "1. Null Hypothesis $H_o$: we assume that the time series is not stationary. \n",
-    "2. Althernative Hypothesis $H_a$: we assume that the time series is stationary. \n",
-    "\n",
-    "If the test statistic is smaller than the critical value, the null hypothesis is rejected and therefore the time series is stationary. In this case the the p-value becomes very small. In python, there is a package: **statsmodels** which has the function of **adfuller method**. We use the adfuller() function to test the stationarity of the data-set. Regarding the interpretation of the adfuller function, the first output is the test-statistic, the second one is the p-value, etc.\n",
-    "\n",
-    "**Excercise:** We take the time series and the noise from the Excercise 1 $Y_2$, $Y_4$ and $\\epsilon_t$. We also use the single differencing method to make the time series stationary and plot the results. Later we will also use the least squares method (best linear unbiased estimation - BLUE) to de-trend the data. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ede822f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from statsmodels.tsa.stattools import adfuller # optional"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d775ad7c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test statistics:-15.24, pvalue:0.0000, Critical_value(1%):-3.44\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# we apply single differencing to y2 and y4 to see if they become stationary.\n",
-    "diff_y2 = np.diff(y2)\n",
-    "# insert the first entry/element as zero\n",
-    "diff_y2 = np.insert(diff_y2, 0, 0)\n",
-    "# plotting:\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, diff_y2)\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time')\n",
-    "plt.title('Single Differencing')\n",
-    "# it looks non-stationary. It looks like a cos function, can you explain this?\n",
-    "# how to make y4 stationay? (different methods exist, we now use single differencing, later the BLUE fit)\n",
-    "# single differencing\n",
-    "diff_y4 = np.diff(y4)\n",
-    "diff_y4 = np.insert(diff_y4, 0, 0)\n",
-    "# plotting:\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, diff_y4)\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time')\n",
-    "plt.title('Single Differencing')\n",
-    "# it looks stationary, but we show it using ADF test\n",
-    "\n",
-    "# OPTIONAL\n",
-    "# Stationary test using ADF test (this is optional)\n",
-    "# show that the single differencing gave stationary dataset:\n",
-    "test_diff_y4 = adfuller(diff_y4)\n",
-    "test_statistic = test_diff_y4[0]\n",
-    "p_value = test_diff_y4[1]\n",
-    "critical_value = test_diff_y4[4]\n",
-    "print(f'Test statistics:{test_statistic:.2f}, pvalue:{p_value:.4f}, Critical_value(1%):{critical_value[\"1%\"]:.2f}')\n",
-    "# Test statistic < Critical value and p_value is small so Null hypothesis is rejected => Time series is Stationary\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9aea5c77",
-   "metadata": {},
-   "source": [
-    "### Exercise 3.  Autocovariance function and PSD (Video 3)\n",
-    "**Introduction:** In this exercise, you will focus on normalized auto-covariance function (ACF) and the power spectral density (PSD), and the auto-regressive moving average (ARMA).\n",
-    "\n",
-    "**Background knowledge:** In python there are functions for ACF and PSD. These functions are given from statsmodels.graphics.tsaplots (plot_acf). These functions create automatically a plot. Regarding the PSD, there is a function from the package of scipy (signal) and you need to use the signal.periodogram to calculate the PSD. These libraries are already imported in this notebook. Alternative way to compute the PSD is based on the least-squares harmonic estimation (LS-HE), which is based on hypothesis testing (see optional materials on relation between FFT PSD and LS-HE PSD).\n",
-    "\n",
-    "**Exercise:** We use the above functions to plot the ACF and PSD of white noise time series. Later we also compute them for the ARMA(p,q) process. We generate a white noise process, similar to that created in exercise 1 ($m=501$). We will see that white noise does not show any temporal correlation. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "89e81bde",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Frequency')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ACF + PSD of a white noise process\n",
-    "mean1 = 0 \n",
-    "sigma1 = 1\n",
-    "m = 501\n",
-    "time = np.arange(m) \n",
-    "Fs = 1 # sampling rate\n",
-    "\n",
-    "# simulate a normal white noise process with the given mean and standard deviation\n",
-    "e1 = np.random.normal(loc = mean1, scale = sigma1, size = m) \n",
-    "yt1 = e1\n",
-    "\n",
-    "# plot the time series\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(time, yt1, color='pink')\n",
-    "plt.title('White noise time series')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('time')\n",
-    "\n",
-    "# plot the normalized auto-covariance function (ACF) of the generated noise.\n",
-    "# explain the plot of ACF (do you see temoral correlation in this time series?)\n",
-    "ACF = plot_acf(yt1, lags=None, alpha=0.05, title='ACF of white noise', color='pink')\n",
-    "plt.grid()\n",
-    "\n",
-    "# plot the white noise PSD\n",
-    "F, PSD = signal.periodogram(yt1, fs=Fs, scaling='density', return_onesided=False)\n",
-    "# F, PSD = signal.periodogram(yt, fs=Fs, scaling='density')\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(F, PSD, color='pink')\n",
-    "plt.title('PSD of e1')\n",
-    "plt.ylabel('Power: PSD')\n",
-    "plt.xlabel('Frequency')\n",
-    "# The PSD values seem to be the same at all frequencies (no frequency dependent).so it looks flat indicating that all \n",
-    "# frequencies have identical contributions to construct data (variations).  \n",
-    "# Think of the white LIGHT that has similar characteristics."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "498f79f0",
-   "metadata": {},
-   "source": [
-    "### Exercise 4.  ARMA: MA(1), ACF + PSD (Video 4)\n",
-    "**Introduction:** In this exercise, you will focus on special case of ARMA(p,q) process, namely ARMA(0,1)=MA(1) process. You then you compare the ACF and PSD of the genearted time series.\n",
-    "\n",
-    "**Exercise:** You are asked to generate a MA(1) time series. As you know from the lectures/videos an MA(1) is of the form\n",
-    "\n",
-    "$$\n",
-    "Y_t = \\theta \\epsilon_{t-1}+ \\epsilon_{t}\n",
-    "$$\n",
-    "\n",
-    "You may assume $\\theta=0.8$, and the time series is assumed to be stationary, so $\\mathbb{E}(Y_t)=0$ and $\\mathbb{D}(Y_t)=\\sigma^2$, with $\\sigma=1$. For generating the time series, you need an initialization of one sample generated randomly as $(0,\\sigma^2)$, and then use the above recursive formulae. The variance of $\\epsilon_t$ is obtained from\n",
-    "\n",
-    "$$\n",
-    "\\sigma_{\\epsilon}^2 = \\frac{\\sigma^2}{1+\\theta^2}\n",
-    "$$\n",
-    "\n",
-    "You can then apply the ACF and PSD to the generated MA(1) noise process."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "99ad0a76",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Frequency')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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Xxd81SsyWNMNtnb5ObLyMGI8YwEBfsXTkifr3ySgBPEhRk1SbYADt9hhLy5ZbZAC1Ys9WDiqgvFA3As9evP9c0MlzrFMCMNgAmLEIpc6RtADhIQJ0VuTMvEDIt17NxNtp907dtAGi8MKmMKOcYRBsAHMIl68L1YaEbR9AJ3rcFEFTPb6PpbyrN8TZCa40Ktgw8m7exdM0mI9fuY6ZGeAbbwhbRhaYJMM6yR3SH7Ii1vY5GoGbibdT9J6RViWAqsoAggQw+5icinNhHzCLA0uOvNe457MPoFNQxGAx6fXVtnrvfOPgHNOqbmQMePN4Y8DUDBKAVLfo8ZjywP1RQe/Fa829P592X2vzf+x7vXWi+b0ZTUkA7VYBpSyWTAuRLKgGFVCxePs94NzlbO9IsV6F1QYwCwO/Wk13M5yaTunAbUa9XZTybIzXZAM4fVFMwtLdzqVqa4e6ZTJqX8eJTU40a3e5fQ8YHW+uzLZBtQEoHGBqGrg3Apw421y21Q5kACpMEopJFZkFQQIoGBNNSABjhgFZ9ZEA2vSB3zklfOxtmKmK52ciV0S9o9VqwKnzQhpqF+o2Eo0uF0zSQinqsnUJgMzp88ZU1Da9lWzvmSaoLHS+c0rEyekkyVJf9NZ/t3g4S0sSQHNFpudvKMv0vGkVULHftbu9gGq1RlyTLJAqhpLCP712ArcJ96KD3as1oBzRVK2i7k8habt2U9CsqjEOHQMWDIhV5uQ0sGtLPjTNzAAPlE1fNcNEOG05SFymUVdcdQbgmmRaXI25ICWAcpNrplZXdw/GgMWdcti9wnzz3AjW6UZgZ59rstPNVIXnU7VaiGqwuxmAXIFm7Tzqap9ZfHyfYHDt+sClqPypKWCgX9w7dQE70N+gU9Jt0mHXVQw5Enj8bNxgamKQMykMgA0MoP7bRWs0GKdnhFpiwYAXyU5IFVvWvmJiZs1MVp3iQADEu4nmhdUSsjIA5tl1A3WpUFtRAfVEDCDEApplyBVoVu8D0yq2pjGFetrYi1kp9INUS0wqdoCpafTKXuldvxyXzrre2kRDqgpIZQDaQJc2AJcK6I133WGGxybETl0fsMXG44tWbUEdxQDUSa9gL6A83pWYmnbvVTDaAORFCyqgelC95rJoBV0uAUSTd7OrOkAwgLffi094UioQP5T7TVGZjt5eYctQdfjMjS7pu7LyEWOv3RQddsVSdzp956vJS8qkFlJ/myQA04pahyx6IsWm8fpR8ff5ve50QPOqBuN7HTSZtwRKegEByerNzABTKdIeIAz8tuNU095tpU0fjAr72NiEGM+x/pAiueUiAchpOEgAswubCujmXbdXjc4A9NXubLv89UUSgEYzZZUAfDrxyXPAsTMeeWmZyTaJbXyxMCbTpFmt2Z8lXtRv5/gRMuflMAJnyapTJQDVC8i2g/vw8QazteUFAFeux71ifOkwFJkJ750XdrRpw4LQVwXULKrVQiWAwAAAbaKpAsdOA0dP2d8zTUwqYhPuLKz8pDFaZQDMjY/rrVvNUQWkMwBJg8oATHYTZhgnzYQE4FIBaWVnmVRsaFbXLJNb+0TG8jsKmkHeRuKEwW1aQq9XFq+YVu0qtndtebnKaLb4mVp+J6s1ge5mACaOPx1NFpOeEkDNMLnE4nvAfN0OaCuX7CqgHGmxqYBUw69N1eOabH0mYr0eebra2aSp0XHz/hAJ46oyQ4fopPlflwCyqsdMth1pqM8ykecVRyfBhCy++a59AK24vkrPsm6TAIjot4noOhFZ5MM2w2QDkPd6HEGp1I6QKgHMImITvWoD8FUB5ckB9FW4wYhqsgFUa5ZVM+JpjfYDSz3TGIDP4E0b6IeOiYNBki8a3ktZMZsJyJK4vZCkyHDQeh1VUtOkL/mOvr/DB+2SAGZsqknXWG9SqmNW3Mm7TwL4HQCfKKz0ugpIuSelApe/N3MjaqGpg9tWJlk66f3RRiTNNBhdDdWQMxklgImpdFrTpAqTBKBHxGRNrQNEDMBDAjAxAJsKKI1WNY/xSfeGOBNNzvSG97KumNV3OgIqMeSmzWSIN03ccrxlcQW1SdpZYZIA7o34qYZYs001U26pSyUAZv46gNuFEWCyAUgJwBWWlrkhIairy/qHzOFLvnlcxNLPgoQE0IQb6PgE8K2302Pe2DZx1bMySAA6U03z93eqgBxumQkVUMoqVP2Gh46Ks4YTg1/SZyhPbsQzLhpMzLkJVUlncQAB/UAYkxHY1PYmtUq5ifOFW7Wr2F59MCpiGp25mL6As3my+aB+tGZx0zBxwcYlItoA4CvMvMvy/AUALwDA8PDwngMHDjRVzsjICIaG4jspH0c/llMPRriKQxCePKvQg+3Ujxs8g2MwBxXbiX4MgjCIEi5gGutJxIeZ5Br6qIQ3eAz3IT7uelSwkcTK9wRP4BpSJs4I+0jQepDT/dR3oA8PUQU3eQZHI5r3YAD9IHwTY/U6peEKT+MuqtjhqL+k6zUewxjsqzXZthL3uAoCsBAlUMQcLvAU3scU+kH4MC0AALzBY1iDCoapgilmvIzRWLnv8gSuYwbb0IfVVMF9ruKN6NttQi/WUS/O8CQuYrr+zhEexx0kJyL5/GUexVQ0C8h7VWYcxwRuRu89hQEspnKsjSVWowfbqB9jXMNriB95uRcDGKIy7nEVb0Z0bkYvHqFefMDTOI643WABSliCMi5jGgTgea3eJpj6djuxEmXspAG8zmPYhj5UwXgbE1iIEvbQIGaY8VL03ZagjCcpvhHvJR6p12QQhGdpAa7xNB6mCg7zGB44+pXEyMgIVg8tEtFfgVj7urAEZUyihnFl1n8Og+ijxiT8Pk9iE/XhAVdxDlN4PKLf1I92oB8PUQ8u8hTOIFsolQoIH6UFOMOT2Ex9eJ8ncQF222Mr33n//v2HmTnh79zxDEDF3r17+dChQ02Vc/DgQezbty9+883jQtUy2A88ExV/4apYeQ8vBx7daM7s6GlgchIYnwKGlwFXogOhB/uFL/Hu7cCSheLeuSvA+Sviett6YNVKP4JfjOrp46P+7hngxh1g6SLgiW3i3qFjqI6MobzvGRG6+vSF9HxWrxSbys5dAdYOA5sfsdOl1hFoGG/lSu6dU/GdwAsGhNT0YKyx8lnzELBlnZA6XovMQLu3ifa8cUek/+hTIt+vHxbPH90ovs2Js8AHt4ChwcYJU2cuiiMnN60FHnm4QevOzeZ9C/L5s7saO6hf1PqXbP83jouV4bJFwOPb4mkufyCC1Q30Ac8+Hn926JgwEC8cBDasEd/ozCXxzsplwI5NZpqe3yva6RtvxOttgLFvp2FkDHj3fWDdw0KFtX61/7vXbwPH3wf27hQulCUS/eHeiFg5l0rAx54WaW/dFeNFxXO7G5sXR8aAw++KcXH1BvDkdvEt5Le990C0m6nOTz4tIvKWCFgwCDz9WDrtpnH18ltxiXb9KuD8VRH4b8s64RUIiDApy5fE8zt6WtRR9uUsmJgS0vaWdWJ8blgjyragqe8cgYiMDKBoG0CxMKmAZEdwGUS5Jp6XS3G3RrmhQ1PFtAQvAyUM5aI5N1DpyaKqwK7eSMbB18M4vPo28NKbCk0a3bWamBhi8ZMM6pEaJ3XkJjE8i+itqniq1eQOZC8vIaVcZjFg5QH3LjdI+ezBmGCK128rCVNob9Z+5IPzVwXjPXlOMPw0lZ4JpnDQOkx9z1SvsmIEfvd94P1LYoPleYcdTPaBUjlnI7D0BJwCpqft6QBzkENA0G50ClDL1VVA3WcELhZGG4BHfKAaNxiAOnDUTizRqprSq2PLySmuPycZqCuLG6jcS6DH6n9H2xehk6VPIKbYPURAWfXbN9kAuJG50YCq2QBcm4EkE1cn+FeOAN98M57Op31UWsYnhVT17vtauR7famLKzwgsPUTahYoWBGB0zJxOx+QUcEpKkwkOkEyf5iVnYgDTmhrE1g6qDj2LJ1fafbUvv3fe/b7J7gGIMxFcbsGAwsC61AhMRH8I4BUA24noEhH92KwVzmyOBVQ1TCyJdxFNZuX4arL+IW0TiucXzhwUS75nmAyZs7mB6mGtravQlDz1MmuR1JQWQbVWS06QJtdRHzdQyQBqVRHCm9kyIWVob+bGN6/7b6dM5PEbDTpdTcgcf543M9AZwIjnmQN37jekv/r8r9dH+546ZPqrN4E3T4jruhG4lvxGNiN+3YDsOY3ZvnN9X0PUX9KCFJpoaMUIXPeYm30OUGgsIGb+gcIKt204Mq0sdTCLj1YuxXffyg9p8/jw/b5TKWKnDSbVE+v3HVDTyjrMWDbFpJFl86JRGYBNxaPfN0oAHgNPjqv7o0LvvsGi5/ZRAanMSN8r4lIBmfLxkRh0CSDvuUFnAGmrVYkYY5fhoKOfpnYwta1MJ21jQJyZ6v11phafqUbGsAG9cQnA5xva0kh6pcQ8ncJwYu+24AYq1ardKgEUCjmx6fHM1VXp9Vv2FUxdBWSQAFo9vCPzDkeTCkh539sNlJMSgJUZpYjTJh9qgtkGkJAAtAlfZ6hqGpvUo/6WsZruWyI9Ztl8xNxoE2nzSZMW9fe9JADDeyNj7rAKWaDauCo9DqlVp0NJ19hoEv0xrZAdDEDdFzIYeQrVDP1VlwBOnsMG6m1817KnDSDtBDr5Vy2vbFiwxN51PHPh7gNhewGSe2ZmEd0bDVR+sFIpPthk57t5V/x7ZFx4lejvSnWGKi5SigTgy+KzviLT1FhMECPj8U7tLQEoadMYgI2uWk0MSL1MrgGgeGe32gA0mtTn718S/xYvTL5rsx3ISdPWDq72qVZFJEuV1joD0IJ4GScB7d4FZX9FVhvA4XfFXx/PsDSoeZdK/kwwdri9LgEY0rskgIF+MRECQH9vI3/9e+gMoK9XMMN7Dxr0ezkDeO5KrhoWUvK5HPutIjZvlPI9WCcDulcCqKskdAlA+wimFZfsBHo/qKuAWowF1Kz3R60m3A6PKW53mRgAkiEb5GSnDzIbXXKVlZjIIdpr01oxgAf7zR4UpvMUTKvTqstYz/EJ1KTOAhSdb1W4j5ra6e1TcW+OGjdsR/rpZK4VYlYkVEB5Tw7aRO6bv95GsXctEl0ij+i5XF1XeuL2M50WnYkMRi678sS5csmvnX1jQtm8+KZmhDvyB7eUx45v74IqCZcMc8ksoYsZgCoBqAPN4L1ierckLWAKjGeiNjFwsw58deWiG7FNK0lXProEMK2sdn1WiTYjumSai4aADz8BVCpmNU7NMPGZyjUF8qu/Y7mvG/fk97p0TewhuHI9+Y48MEZd5dc9pbTBb2wfR5vduS+Yi223rL76zBMyu6cfy8gATBKAYxKs1ZJxtfT0e3eapWeJtJW7rxeQzKeUMu3ZvPikzeK6IXiBLH96urEzXL2vQ5WESyXkerBOBnQvA5BfNk0CcNkAdK4tN0bpOvysh2Xn0Q90FZArtIX6js4ArC6tFiLlIPOJFWS1AWjlmMoyHdFoUnupk49u3NMPjnG1u5q3jP2jT2TNSADjk40zpvXy2jkhyLwH+5NjwOc9iRQvUHHilaZpVtur0iM2hbnOetYlNzVNlnOJq5r3lg1pNriBfvvzI++JzXCmvGwIEkABkN9Nj9+T8F+3iLCy40k8ulHsNlXPB5blZNYZNikBmO7JFXSPx6dWReT65Kzk52MDcKlm1HawrRyZtcwtEyFr9OnXkn7V0JjIR/suumeMCpUhSoZhaqO8oLdDuyQAPZ5PGtTx4CsBuGJAyT5RlwBM6j7dnqSUUyr5jy8Z5TNNAlC1Aya44hv5HIWq36cgAcw+6h9ZW3lkkQBUSCNWiewSQFbagOalAbU+vhKA2rF1H/3EitRC2IxBcjChZJk4EhJASl4qXSZa+3rddPii3p5VRQKQ39nWRvAb1Kb+keCDeTMDbpStL1pc0NOlMYBqLTmRqn0zwQBMeThUQKUmJABfrxtTukqPhQFEf3V1l3XvgVZOkABmAbUa8N45MYB1Lm+TAGxeDLoEIK9NxlKXeGuCj6olDap6pRUGYJvcXBKAKZQuoLVXKclkJN16+6W1gY2RAOK8ZD2dDa6JxqQSa2UTkAmueqtF5HHAja5G0etQqzVCXej31fdiDMBQTs3BAHQJwDaRu1RAWSLwjk/E30mDKV1fr3lvjPyrjzO1v5w8B5y9FE+vlqPeO3vZbGvIGd3FAG7dFbsP1TCvCQagDwSL+kG3Ach8StpqqmUJwKNj+6iAfHZLVhUR2WTgjNWrFjd2qXmoOlAVsfZySAC66iNtdaqrYNR35HnJejmm366J1fc0qEQZ9iwTqGqSY+xd5Ydtp2oWyOxsE++pC8LtVD8bO8Y4YPYCUiFVQGr/U1Vn+hhyuY2aftclgORrsfQ37wC37zsSGWCSAHrKloUCN56rUBnmtZsNN2BTHVRcuCoC7rUZ3cUA6lv4y40Ok3Dl87ABMEcd1yABqCtb/VmtZj88RHXfzFuXbPLEMEG2T4/ix69OrmrbnL0iJvoH2uYqk863Dq29dCYjmYIuAWWVABgKA3CogBIMwCEBqGm3rQcWD1ncWHVaPb5lPbyFbny0SAC2DU1ZoK++9baQq3/dDTrWtz1sAFIF1GtgxPriqEQwHrHqYqo+RuDzV4BjZ5LGexvN9bwN/bhHC/+i8z6975sWDpevJyUwNY9ZRHcygJ4yGjpQJQiVabXp6wUkGYlJApDPzl81HzYCCOPRzbvmstLAsE90dQnAwQAei0ISywmw0mNWz8SMxNG1fnZyrdbYpKWHzk2TAGRsJV31kbZLNaGCUJiVi/FlkQAAYMUSEW571cq4qq9VCcCkfkyogFQJIC8GEF2bvICkQTzxfXUJAMlJMJY+WhBUFAagHsWqq1HTJIDpmfgiyocBjGpeVt4MwHCvXNZo1PqAjw3g9IUkAyC1IWcPXcYAlBgu6qoTgNV33MgA5HsWG0CaEdh3RetKe+uecmoXpzAAgx5WxbLFIvZ6VZk0bef1JvI3eGgwAwsXNAzjJqgSgFrW7Xtxt8hWJQBbvW2rVRcqFRHPX6ff+b18GLjWDvKerd5pfvHeUKRWGwNwSgDwlwC2PCL6GCDO0ZDpdQnA2MeU65ffEvsn1PJlFrb20u0WMZWj5R2bd1GaBFDyNQIbyg0SQJuhSgD1D6YGoTIxAH1Fx2YJoM4AtA7GSGcArlWjrVMfPSVCIsj05ZJ5tSsnQ5fng76KqvQ06qkWbzz/WJeY1PbRyowN9ohRqhOdMbKq5buoSExA3Bh4WeKspE2sPjYM07dMswFZJQBDGsDfYyetTFUFpOcp219XWZpWri4GICWAhQvEwT2D/Q2mkpAASulHSOogbSFmgtquqvpXf5bI14ByZANI2D0sbaC2a91OqDBcGaBQlQDyVAGnoDsZgOpzq9oAfAJiyff0jic/rm4DMEoA0d/pGXGikO47bNP9psEkBUg3UJcEoLuhSXWRHpbBJp7HJqdaQ73gmvj6ekW6qWlzh1ejQ9a/lVaHkqK+k2mBuDRHpUboAJ1uHaneNTYvppR8fRlAQvVlYSzvnhFGzVbAaFSHtPwvXRMGS8CtAgLc6hfmpBtof5+dAVglgBQGoG+0HJ8QDh8Sap76rmEbA9AXDju3iNPe9LPA6/O/ZfJW5xRVrSrTqScE6nnNArqUARgmFZ+VZvQqALcEkOYFJMu+eVd0VPXQCfV5rMAUoghmBiA7W9pKWF+dA4gdzgLYJQB9dSpXvS4JoD/aoDUxae7wJQMD0A1stlg86rcskTjuU6puJJphAF5eTAbpLo0BGHdEp0iJx86480yFJgGo+Z+5ZC/X5AYq0+kMy8S4+3uVndc6A7BIAC6o6zBZ3uvH4sdQqjSr6l/APub1/rt4SGwqrPc5XfWp0aCXrbeNLLf+DZQ0eUh4nugyBiCNT4BTBaRPNKPjjVW6KgHExFclv4QEoNEh85C7c3VPGtP8/+4ZcY6sCRwRYNK5+8Q/sUkz+qC22QBY/81mBqBC7tCdmDKvwuoSAOz6fN2FNzbINBWQTotR3Zdh8rGFsjAyszQ1lCWftInKyJBrwFsnGzGMrEVyfNHiWsXH8tclAAu9khYgPp5iHmbawqREZgO3twRgo1npX/o+HV8JQC0PMHx7y+Ste3jVTz6LftelMKU839DcOaBrGAABjVOPWOEA6iqy/pE0XfqhY+Jf/V3YV7dZbAAzlg9tmlBu3BEHj7vQ35e8V9UmQhtiKiClk6r92VcCkKvLitaOMQkgYlaj41G8mLIIpyHhJQHoDACN3/UVVimetk6noe1t38NEv68ROIsEEJs8DCvqBL2G7yHDJNePbbRAzc65E1ivj8XFWbdZnL0c31fSeCHOsGNSlec+AL18XQWkv5eQAJQ0LhtATMJX7hvLstCqR9dV1atqfmoeQQLIH0uhxah3qYBsfuzTSlx4105gXy+gqrahx7Qj0EsfGJUhD9VQ4RsB0aYCSlstmdLIOq9aKVwnTSiVRPC8Sx8Ir59yGRhe3vA+MX2XhARgUQFJutQ0OgM0qoAySABUgvlcYkO+PjaA8cn4xh99QjXl67rnU6ZrH0A9nfbbZAOQ6dQ8Ll4zSwC6ysjkGGCi1YbYRG1Z+ZtsADfviMidLhWQiQPoDMD2V2JaC1oo26KqMQDVCBwYQP7oUz+mOrhiHdLQYVWoIQ5s8TsSqykHA9BXnDYvnjTINMsWASuXxZ+pq7Dh5cIbwwSVRpMBVs1LxbnLwjVPom4DiMpct8pcBgCsGY4mv4lGmTJNFiOw0QagrT4TEoCJAWSQAGIqIEu+WSbjU+fjh5GrUipgd0fW4Svxqatv105atU0vXI3TKN+Vz9W+snSRWQKIqYxgXngkaHDUw6QCkjCqFqOF4LEzwk5gVQFptKRJAHVaWbhVP7NTLGam5LnjmnbBKQFoNI2NA2+ewKI2TNfOE8GIqB/AdwP4GIDVAMYBHAXwl8x8LHdq2oiyOltXa8AHkZeDeh5nfaLRJmLp+zszoxlvTBIAmSUNFfKWLsIbYwYZ3jflTxENDy8HbtxuPFcDYD26URie5Y7jvTtF55LvS9hUQD4hCFQJADDbSeq/oxvTM8ACTXqJfRfLpObjBWSTAIzSTAbda33XskFVI/dnrB02l63DpD/XV9S+Zw3Ib+QT70aXAK7cAFYuSdIGiENQzl6256Eyu4UL4kzYJgGoweAAezulSQCwTMomBq3SYrM/yXxN5Ojnfsu8VUZZiqTx3kry7Iiy5kVkkgD0ekxOA/dHUGpDxDgrAyCinwPwPQAOAvgWgOsA+gFsA/ALEXP4p8z8du5UtQGxKf3C1ca1adJNSAAGMdfaQUraKhAOFZAlyJXLGAgYYsbIJbehLH0Vpj5eMKBMvB4qIHlweJrrn1XvrU/g1KBRt7vUv0E0UZRKBgZgW41xcoWVthfDds9Gfmzi4/i+Brk/QzIAXzdQFfrk6FpISNwfBY6fzVCm0ja1mpBCbt6Jf1/513ZovEmlI6UjowSgjSWT5Gmk1QK58DGlMx1O5ArWFss3sVqJ36/nqbWT+t1UBiCfS8ePas3QJyVNGlOKForTbdgp5pIAXmfmn7M8+2UiegjAutwpahNKetyS+gO1A8t7FvEvpo7QdYRKfr42gESUQ+2vvNZpNkUj1EVUCclk0iYEkyguPXrKkXFubEK4mqq2EB2ZJADlWh+YuhGYKIMKCEk3Ox8VUCpMbcSNNkocZ6nRYIPOaNV862k8dOOq5JcqdaBRHTXt9IyQeKcVj7npGWGrMcHGAKrV7DYAU+wdmRZIBqaT+dXHp1Ifuf8l9gBJdZTLC0htX5sKSB+zap16K40IpLoKqFo12BAVJqIiGu8zbWAAVjmRmf9S0EWf1p8R0aeZ+TozH2qlcCL6BBGdJKLTRPT5VvJKQxkwrzDIpAJydER1ZWlc5BomJZcEoB5CYvMqSaxsNAagPk6VACwTg1EFJCc3ZXLu6zXXW62DjQHYjtAEkgfWJBgAkhNEWW9raO9QowzfOO0umCQAafNwhSU2OQuoSJNGXBJXrYYVUr5VvcBkX7JttLN+I8T7v9T9W1UlMh0aTIVK8QnYKAEYGIBVBRT9feWIoXxCYlKux/cybNQraf0x605gmwSgSgIyTaWn0f4mL6CEYwKE0VjffR2p9WaVASj4gue9TCCiMoDfAPBJADsA/AAR7Wg1XxvKiFaQic6udUgAKDt0kWonM3US/WB4daWl5nPrrohpYhTzNRFAXxHEGIBWiF5Wq15AQHzy7O91r2il3cBDAxQX/y0qoPrEQnYbgKnd9N3PtnNp9bycMKgr5KrfpI4ySQCLDEZ4ow2A4+/bwlGfOItdNCCkM7V9qlWxcnzliAg+lnzZLqWpbWVV58n0WVVAGv0tq4AMDCChp9eYqQrnPgAD4/aRAGT5fRVBw4xBGtJVQBRl8vIR4MTZOC2RBJBXBCgVLhvAJwF8CsAaIvo15dEiADkEJMezAE4z8/tReQcAfB+Ad3PIO4G6BKBLkfWPoAw41wEWunuhDnXyLEf5miSAuw/E9UAf8MjDwN37wJ0HdVIaaZHspLHncpLQ6xNB9wpJW9kAyuQrVUCaBGCD3GTjWl1ay2xGBWQZjGPjgsGqE1maBCBVOC6oVSnpDCCjBKC6POpSXP2e0tdsNoAbd5LpAfHdZf7XbwNb1yfzN/YZiuJK9QCLBkUM/USoEkPdpC1EfieuJSc9PX1CAmiWAVho0lVyJlhtACWADP1BZy46jaoNoC+SyNQDqFQJQG93G6kzUi3nqkhzcNkArgA4DOB7o78SDwD8tzmUvQaAurPpEoAP5ZCvESXZsXVx2rThyNUR1VWZaZJLSAAWBiDL2LG5YSySB1akqYASz5VnNhuAyQiswji5cZxWQIi1tkld1YPX28ZBW0wFpDMAy8oylsYy6UqDpWorKVsOJq8/LyddHF0oKSu5mASgliHTGhhATxmYMhgpVfpYecfInFSJR1mc9Fbik59RegDqHyexmGFgaCDJ+E1nWcQmdJVehR4TA1RVexJWuwW7VVBWCcDwjr7qbnYncMLupPytM4BosTShtJvNCGyteiRB9PS0hQEQp3BIIqowc+5FR7aF72LmH49+fxbAs8z8j7R0LwB4AQCGh4f3HDhwoKnydlUrqJR7sAAl9CgNf5jHsIcGcYwnUAbwKPXjHE9hAzVWujPM6CHCWZ7EA9TwBA3gDR7DQpSxlfpQY8bXIcI5PIwePEr9eJVHMQHGhzGIO6hiFTXioR/hcSxFGWtQwTei9zagF+tRwYsYxTpUsInE6uEET+AeqvgQCdXBQR7BUpSxm4T3zqs8it0YwD1UcQKTGAThWWqoGSa4hn4q1elZjBKeosF6XhLb0IfVEY2v8ig+TAtwgiewChVUwVhGPXV6NqEXvQaD3TjXUIZgtlcxjTOYQh8Iz0X0nOZJXFJ6sUrrezyBK5jBcxhEH5VwiiexlfpwgiewBGUsRhk3MIN1yne5wFNYR704zhP4QHlXhazjIpTwdFRvADjOE3iMGkHiRriKIXIfmvM+T+JCRP8ylPEEDeAwj2En+jEBxhIqx/J9iUfwbTSEWzyD5VH7yetRrmFBROt5nsIylLFQKf99nsQMGNuoHxNcAwMY0Oom+668XoIyNlMfrvM0FqGMNzGO52gBmBkvIh5uZDf6UQLhTYxjNXqwLaL5HldBEPrmaTCGoz4xxjUMGtp2Ocp4nAZwiMewFhUsQRm3MIOHUMEFTGEz9eHrPAI5zQ6jB49F4+MZDOIypvE+xAS5Cj3YTsnAfQ+4ircwjo/RUOLZJZ7CHVTrNIyghmcwiAVUwrs8gZuYwceV997jiXpdx7mGG5jBGlRQ1hYn13gavaB6v5f9aAgl7KVBvMPjuIUq9kV5TzHjZYziY1hQr1MFhI/SArzHk5hGDTtpACd5AtupH1PMqILxLYiDd57CAGpgLKX4QuUVHsVztAAPuIoXR25gaCjZBj7Yv3//YWbeq9+3MgAi+gsAvwng/9UZABFtAvCjAM4x8283QxARPQfg55j5u6LfXwAAZv6Xtnf27t3Lhw41Z3e+f/BbWLRsqYi7o64Mn94BvPGu8JGvRq5wG9fEfZ6lF8y6h8Xfy9eBpx8TbnenLwhO/vE9Iu31W8IV75ldIgrlK0fExhAZXREAdm0Fbt8VovlHnxL3zl0RJxd9fI84OOb8FXF/63oRiEqGonh+r1BvyGBXz+wC3n5P7Kp9dKPQBb9+tFGW3MPw4SfEiuTeAxErRuYl8d554OoNcf3cbkH31nXAtVsiDxmDfcdmUWeTR8aCAbHa4ZrY5LVprVg1vhp5Cm9+pOEaCcRp3b4BeHiFKHdqGti2QZzfvHW9UJeNjIpNbqoL7/pVoq22bQBWKe+qkHUcHW+0ISC+jXruwKIF4nu6sHFNY2Pbnfui3XdvFzt4B/sFnds3iLNfAeAjT4pNcsuXNKJTymu1vLXDUR2VM3jXrxahNE5fFMbdWi1Ztye3N77l7u3i2567AqxeKfrW048Brx2Nt4PEWycBMPDko+K7y4CEiyIf/kqPOP/gg1vivkkCUPviU4+KcXF/VNTx2g2h2jwX9Wk5wX5wS+i4n9klvsfaqJ+oz3QsGBAH8ZiMwGuHRd+XNCyKxsrouOgXDy0FXnpTpO0pAxvXijEOiHZdvliUq3vkPbxC1Ff2e9l+I2PiqMwdm4AVS4GvH27k/dGnxG9ZJ2bgG2+I3wsGRN0e2yT6S6kkbATPPh59jxMASHxDnY5o7jjII9i3b1+yDTxAREYG4FIB/QMA/wTArxDRHQA3AAwA2ADgNIBfZ+Y/b4oagdcBbCWijQAuA/gMgB9sIT8nSkCki9TVCJpICthtAPI8T8CsewQMHggWFZD0bVfzq5el6RFysQHIsmyirfJ+zMNFo7+nnBTb1RAariBXetEm1YiEvhPYRwXkkmZ1FZM6+QPuE9NMNHrZAEwqkOivekKW1QgsyyJgxlA3fdex6mlSraW4utrsNFJFqvXvNG8ZSS9BccN02ENknfVgcEZSLXnJ/Op5avmoGxk3rRUM6aqyEJMqoFIJCROrbae/be+JrI9aJyLhNDE+2Qh+6LIBmIwAchwsXwzcTAnw1wSsDICZrwH4GSK6COAliE1g4wDeY+Yx23u+YOYZIvqvAfw1hLn0t9u5u7jhBaQ9UDsd65NlBFPnc+0DADQGoCfipJeKTKPr9Ks14J1T2usmG4FB566mLVme18tXOm1sABkYgLopZu8u4LV3xO+yEmrXaCdw2ABcm7ysRmCH4VVH2pnI1nOMU9K7GMD1O+KvSQeuuv/q9BOJ737hYuO3KUKkqR+UqBHvxmXUthnqCWgwB+W+LUxGbPJVGLUsXz/wXF5Le4uvEdi5Y1eblEn7Nmo5erecqUb2GE26Uutvol/fu1GtNaQBNf3ggJBGlkTHpPYYFn2SLlMVZb13bAa+btiJ3SJ8ev0wgD+GMPw+DMEEcgEz/xUzb2Pmzcz8pbzyNaHuBZQwRCoDNy0YXOw92wpBc6FkWCQAzQ9YXxlJ3LxjME5qAx+wSwA6XVavHGWA6JOZ+oouAahtZZJoXBJA7JlrcrcxAG3guxhBmpunlwRgKFuuMk1GYOl+qfueA6LdntvdiE+v0k4UDxGuSlnq4erqpCj97qnUqIvLqK32S5M/OhD/PssXm/MxuoGqhk6t3WWWR04myzB9I3mCl9MIrBEeW4TpDED7FnLjm16fNG85kyOJngYQqp/xiYYzhtrPTMZ3HdVonvByU86O1FyZ+b8HsBXAb0Ho/U8R0b8gos1toahNEBKAYZD7qIBMUPcBmCYGk1eAxPGzouPFJk9lAlE714jGb/XJwjR5GOm1TfzyuXLhUgGVNQmgZGEARokkgwSgto2UovQ6ZJEA0urvJQEYJquqXHlrqj9b2fKyVBKTuVyts5Z+bDz+W9bx0Y1C9w9oKiDFG0nWxXl4vCoBlGK369+87s1SiYfqNtVN9wICxKSnf1fXd1D7z9phYddZvjhdmlFDtahQVUCmBRKh4WFTp0EyRcv4NtXXxQCGolArkqHHJFFtAWjqxjPVJBPNEV45s7AUX4v+zQBYCuBPiOgX20ZZnqjVhJW/p2zvgOrE63OOrGlVB8DbBnBvJP5hTR1Lzaf+W3vOWg831c+0Mk+k0d5XY9u4bADqxKkeSGMbcCpMonWdnmig1NvDIHG5NvwAyfMRPvKkONpP4uHlDVe9zBKAZSOYSVViqqcqkdU4ruKRE5Nelk6IWl+pcydq1MXFAFTVZJ9qj1BsOPL5QL99UZQmAaQxANWwrJ4fsWmtMOLKvSVWewbFaQAa4yOmprGMj+mZuDrOJilIxBZ4Fo2BWmcZfVdu+swsARiYaI5IZQBE9N8Q0WEAvwjgmwAeZ+b/EsAeAH+/bZTlCSkK91bsk5C6kk5bLco0pnRyMKlGRlt2tg0yYPtH51p84CdUQIZ3MkWGlO9QfFCrealp1WeqesLUNgn1m4cEoDIh2/v6wJd4Zmf8d6Un3ua9vbAOYiMMEoBuA0iTAHQ1o1z5qYxDpu+tCM8WnYHIn3rMKWlXkrSYorfWasLjZ3K6UR91c1/NMA5c48HIAKJ7ptWrnpc6TlTDeD29QrcPDeKi8SxtfMzMaBJARK9qBDZJAOpCTO87ah37+0T7yrORbYsemwSgn6mcM5zhoCOsAPD9zHxevcnMNSL67vaQlTOkgafXsIlJ7Ty1aOL14bg2G0Bfr7D4330gvA5UA60Okw2grnYxeCaozyV0lZFRAnCstOv3pTpFSafG9je/lF6Oc/IwlK/m1dPTCDxnY7iAXQVkGji2QefFJDX6ACXejfbbVmbCKE/JFW5d9dIr3Br1CUjtK2q+LhWQZA437jTcfWU+sXhUaqyrDN+wrjpS0vusXjeuaVzbdPFpKiDbjnBlkW6sC7OYYCs2G4BjEaNKAC4VECCkgMkp8V1sY9HWTG2WAFIZADP/rOPZ8XzJaRPqDKCCREvLn2cvi0MsXBONCpsXEAAsXij8o9MkCmuQrCh/kwSvq4h8Vms+k1tJHcmIq4BA4uwAuaNRlxae3C5WOrfvJevjUvO4JAAiwbBlMC2TWx6jMUEAnrYQW/nN2gDkhKn9VhHzNNMmDdMEl2g7faIwMJuYCijKWzUCm1aS9ew1CUUv1jUBJVbfpLRN1T05Di8XDM70TL3nVAGZaFD+usaHZJBGG0Cso5jLsi0e9N+SwepOKLqaybSQmanGJeuc0T7ZopMgT+WppKiAbt/zZwA2CQAQH3ymmhQ/dbj2Adho0OOb6DaBpiUAWb78jQY9RMKbQfcEke8sXhhFCfUox/S+Lb0MkSEnetuKzDRwfKSWEin+2RklAPmOeuAO4KEC0lyNS5RU6+kMVn9mui9j75TIbAN4+a24Z5FOl0qfbO9WVUBGpqPk5at2c7qBKnmaDoCBNgZNDKBiUAGR4jJuWjSoba/XQ5+wpWTjVAHBDJMdJUd0CQPwUAGpv70nL22gSuh+2M1IALb3ErHjpSjqoNkWjTGWRpMA5MAzZZ3GRNRrX6Ygy9+2XuyqrfREDCBFBUQk2vnoaa1dfCQACKPwzs1xPfjTO8QuzzSUSvGTnYjSjcAJTzNK6n71tpO7UdVygKQEIFWHdRWQZgO4fttO154dYhdwbCOfaQa00FpnAMq9mWrym6k/fdVujOZsAKoKyGQElulNwQ5dC466etRiPxrQnA9k/vp39ZEA9P1COaM7GMD0NGaY/Tw99I0rLtiS6SswKwOwiZYOCUB3+9T19GkSgHVi1CUAstNiK8tnRWODVKEsXyLCBEg3SakCMkpckWpobKIRasFEi5XGkmA0K5bG7y8cFB5CrneBiAHIb4wGM3K9Z7IBJA5a196LeVep0oRmA5A2LNn/ZMRZ9V2bvWRoUEhy9YlNlQDMr8ToqS8WKG4Qd3kB+Ton1GqNQIlpNADKpG9QAZlW0+ot2dYxI7ChDmo76vXQI+bWbRuGb6/SYPs2vvNRE+gOBrB0Ec5D019LNCsBiMTa3wjyg0+nMQDDylyddK0iujbw0+AjAeiDSK68jOoon9W1ZfVkpdHwTEa1lCtJ/X054Zg2PKXVU7/O4q+uvlNf8UcTRlM2gJo5vaThyUfNtKkMQLqSqkZgE0zeRvVyS4iv5O3ZJGitRpuu9O/kUgGZGMCjG0UcKT29zuBjeUZ/TbYgXQ2b5hgQkwBs5SmLI8DtBQQ05gNdsteLsNk5ggTQIpYvwUUZhTKtU2dhAFYJQBPBbelcKiCCmQ7jPgCzxFAz7mvwYEaSnlQJAMl3TNcpRSdpjFCXpGbEu4sXipWqeoC8lQH4SABNrEZjNBtUQL4SQN0NFAYJQFut9mkSQH3C091AIxWQq+7qYUJ6sthk7hgHfZWGnrvSI77T+ESDcbhsTjEbgCH/4eXAlkfs75tgjc8Dw6Sbkp/a9q52tEkAg8lopvV+7JJGXNJZwW6g8wweHcCbAVjStaQCiv5TB/qiIdGxrt2MBrC2wrH0mxoiDt+UBECOvC0cwFff7yrfdE96t/SUha76/UsivorclDbt2PDkgu9EZaUZjUnYlwHUY+RrXkCJjC002FRA9bMbXBMXzN5GEqrqRl2AqBNWjYEPPRHPQ0ZW5aiQ2O5wR7tmdb21ptEkopgtQLFpmejRaTIxikQ7aWNDlQCe2ZXMv8fCAPR8s55NkAO6jwGkDWxbFMAsSHhhpDAKlS7ThrTBfrE1/tpNgwrITkb9kc9OYOMqP28JwNGwRtE8+qvTsGG1kASWLfabdH1oTExU2jtydavnpR56QpTBCKxMApL+Rx4GHhkGjp6x10G9ldgI5rAdyZdjEoCFAaj1UfHMLhHZUr8/2C8M1XKnvVMFZCnPSnLaYFTKM0oAPiogiNDZRI1wzGkqoJrCXEwhZlT4MAC1LwENZmujOSd0hwpIRWp/akIC0JMnVEA2BmBYKZmMaUTxs0R1HafFaFwzhSO2IbHK0CY3/VnsL5Lp8li0uNQ1Dy2L7nkytLQ8E/FWTJOYob6q14y+wq6/b2AA6iSgBnqrVOwrRHnPNOHV3UAjWndsMrwLe1RPnU6ZXkV/n9gro6O/Txjr5R4C14KjVbWbK43RBqClMeZHYrPW0KAi7Wg73vXybBKACTYVkEsCIIcUlSO6kAGkNGYmBmC536OrgFLSqXSZ/KkTDEDJw6Q3jDwZ6t0pVo5Wnl6++tu2j6EdEkBaeturrdx3SgCOZ2oafeesrwQQYwCau7DtG+n3bCogwO7G6qMCks+ck6b6HjXyVvut+qyRcePSZx9AmpMDKfQldgKbvIAckqb6I1Zn29jwXKHLeuoRedPUTPXrIAHMHrIwgDTVTqoE4GIAiHcUKWZWq0kJQCclCoJWkTdj7q8WWkyrP5sEkHV13Sx88rOWk5FG1+rXdp4ykYgGKq991FHyRLHenkaeCTWFYeWp3pP52WIB6WWqaVQVUNoOVl/U6dEWLkBSsjK1qws6Q+3vBTasxlX1kML62JE3XF5AFglAv7TFApLPXLGAdFR6hNpy19Y4Db4q0TzGkq2YtuXcqfCRAHxFLlsy2SHkDmRbR7cxAJm5OuhVDw1V5SOP7FOJ2bQW6OtF/SRWkwSQqItplZOykc3FGLJKAMb8Pd613rflaZn0rWouNZ2hvqbYOa4yV60Uxwuqm4NqyjdX06vvlRVXQhMDqHF64DCZRkLf2Z3wSomTZYVKDwFO9UVWFZBax75eYYBevxrTuvFLlVhdjgvGfmmgz6VelLu39fdthw4RiSM+pYcQKff1cvX3gCAB5Aq1nXcajjTQt2vr2LlFsfRbJgaZx3S0SrGtEEynA6kx+OVAkp4d5XIkAaARV2R0PEnCwgXAh59o2AC8JACDG2hLEoA5SSZ46X/jP2u6yK/DFIEVaNRfToq+EkDMBpBVSjHkoaZX39uxWRi9DSeJ1ZiBmgxJ4ckAVixphCqWSLybon5R61CnR1tAuRiAjwrIsm9hVC5uBvob5dQZqWILSNO7i5vJMkiRAEzjWw81vXOL8FDzgoEWx/oj2AByRdSYSxaa9aRpKqDB/iQnN6FcNksA6sEaWVRAQONweskgtq13EACUJIHNSACAwgA830nTW2aWAFJWSOJB7Fd9urClt7nEEgHP7gIe25x83yUBqNfq4/WrDO8bYKqjabJashB4fGu8f0bfpwY07E2uSXVmRhw089AyMWG5vp9LB26rg2kDozMWkMfOfFUCUOj7ADPCc2flUiVfgwRgs2PFaFKulywE1jwU9+d3MX75fMWS5PkTaUgbD6ZQFTmj+xhAmmEr7aMYjz00pOspmSWAYSXEQNkwMTNQPzBeXxHKQ9eNO4WTRNRzNzEaHaZVvi2WkY/aJdXY5oEmbACpa1ZXngP95gnUJQHErtUJ2+AtY6THkF9qH43+Rt+nCm4wAJcEcOO2SLd6pfm5zhyzqoBkWtcqX80r7ZxmwL1zWZVgiICRMeDt9xqxv9Liapny7esFtqxLfk8VJgmgGZi+vQk+7dQkupAByL+2j0uNydWUxmdXLSAm3bRBaTsQpm7MM0kA0UYw3VZhItUkAdhgEtVt6hTLgti68iPbCynwYgDxnzVftQUA51F7WctOrHx9pBfLM99FSlYJQEp0QwvMz01HevrANZG1Gg1U3/VtpYHEKXtq4DwAyOIKncjTUq4Mla6nywpf1U5gADkibXCpW/RNacoWFYIrna2jmyYZjvS0ZYMEUCo33EAJ7gkMigRgcjdVD+LQaUGUf72TJ2Z6833bBKi/5gsfXqvR7fByT8J3ANo8a1y/fXZf6+/4SloSbJIAPCRYW39MLCgy2gAaLzbgipPjY9xcv6qx9yArI1U8Nb1VQD7QvYCahVOCV373tG+/bvftBJawfXR1sBMlZxRf96zY2Z8ZNrycviD+Ll2YfEYQER7lwTUpq8yyyQ2USHih6DBJADYbgI8EoEs3Nn/uPTscsd59VuEtMADfCSXtPFx5rWbnvZp2MYAU2lQJQD9nABBB5KangWNnGvd6HUNep9lHfQK4JdFWPViIhKpHnqnrSpeAr5rG2tDm23UJoFUVkFuCr6ONEkD3MYBUCUBxz3OJ52lolgGo7+i09kZBwe7cF0aqFFrqpfb4lG9YqVk9anwkAMMK2ETv0KCDJk81igJWPUDS4JxQlGub33aCAVgkIN+5x8VQTYgYQFVdiqrtvngICVQ8GUCWOc31ndKOS8ySv+tVk+TDcPRhNf+U+zYVUIvzv3f/nm8qICL6NBEdI6IaERmWo20tPP5XR1mTACQWDyUPenB+NIcK6LndwLOPx+/pHTjGQKJnapRE/R0DKZdkBFTfcxBsaMYGYLzOOFKKlABi+wB8VCY6A/CUAFxqgFQbgKhtrM4m9c5zuxteLU4GoLe3r47D8Z3y8GDxYRq2NF6TdMZ+WXc5zVECcJE03xgAgKMAvh/A1wsq3974qgpIHRBrh5OTtisvlyTRW0lnJiYDcW9Fo889yZzFFPDxPc0NIJd4apvQrRO2xwoujaY0dUiE+mToM3c5mZ4hnZPh6b99JQCfdra8xykSgESvGr7Zcb6sXl4925QP56qD17GPKfDpNzYVkI8ROK1v6c/bIQG4Jvn5Fg5aHiZPzYiDrSKTBJDS8D42gGY/nkkFJOmTMVdaWRmlpfNafaf81u9n/d4xHuJHQ10F5MMBMtsAHBXWv0cz7e76Bg6kSgBAg4m5JAC5qJA7eodXCHXjI8P2d9S8ZR6xZzlMXj7GelNbeauA0vLX6xT99lEzOrNV8k37Lm0CcauVaKVwooMAfoqZDznSvADgBQAYHh7ec+DAgabKGhkZwdDQELahD6upgis8jfcwCQDYRw1d6bs8geuYwYcwiCoYQyQm8nd4HLcQjzvfD8KHaQEmuYZXMBZ79ggq2Ex9mGHGSxjFIEooAY3wDAaodJzgCaxAD1ZQD47xBG5AbCr7EAYxQCXc5yqOYwIfIuHSd4oncRnTsfxknX0hyz/II/V2MtV9O/qwiiq4y1W8hfH6fQLwvJKHxHMYRB+V8BqPYiyD68QAqF6/93gCV5A8+GUH+vAQNVa1H8xMYLinH2Ncw2vaN9Hr+Q0ege0kgV4QPhKVfZ6nsJ568SaP4Z7y/TahF+tI2GVe41FsRB9WUk89749F5RzmMewhYetQ2wUA1qGCTSSkwSM8jjuo1tv3JE/gqqHOAPAxLEA5mhjOTY1hQ6/I/2UexZShjZ/CABZTGad5sqEaNOCjWIAKkbE/2bAEZTxJwl3zfZ7EBUzX2/gVHsWkRs8+Qx9xYQ0q2Ep9sf6m921ZPxW3eAa3UcVW6sM3eaReG3WcAfY224herKde3Ocq3lD6uRzbb/M4nqABvMXjuGvtSUnswQAWUhlHeRw3o/dWoIxd1HB5HeMaBqMFqGynrONZxf79+w8zc0Ld3jYGQER/C+Bhw6MvMvOfR2kOIoUBqNi7dy8fOuSVNIGDBw9i3759wKnzwJUbYjPM1mgn7YtKnju3iF19rx0Vq6mRaBLZtTUZO2ViCvjW20K8fm53/Nnl68Kjp1QCPva0H5EqHY9tEod437orwgDIHY+HjonwD4sWAI9uAl57R9zfsk7sYDTV2Rey/Of3NtoJSNb95DlxNsGShcDu7Y37zMDXDzfykHj1CDA5DTyzExhU/LrTMDEJfCuq39b15g1Mx9+PHXZ+g2fEJNzfB3zIorKT9fy2p+w66qlp4JUj4nr9auD8FeFVoxpWz14CLlwT18/sAs5dBm7caeT90pvi+unHgDeOi2vdA+vCVeDsZXH9xDbh4XXirIjxtG0DsGqFmb6X3hAuwUS4VJvE2ogR4SNPmleT33hDrOyf3C5OVrPhlSOi7ob+ZMW9B8BbJ8X1xjUi4J1sYxM9566I/rtMG082XLkOnLog2j46HjPRt9862YjlL7F0kSjjzMU4HS9qc8hzuxsqMhVnL4vvs2gIeEo5llOO7R2bgXfPNL6bLw6/K+YVOdcA8TYEhIr4iW3CvTdylMg8nhUQkZEBtE0FxMzf0a68c0Ha5qyETteQ1iWZyQ5lc3FMg80jw2ajyENK7O8VYr/M35a3p0E2mUFmI4BH3nF4qYDkYfOZvYD0NL42AE/1g83Y7iKQPFVAsh+6vK4Ae12dpDjqYKJnw+oMmat5NqHHN4WCqPSYjxH1zVsNfx2jLyNiKiCdAVH20BJNoPvcQMeF2ie2w1BF2TbBWnSMNixxrLJ8oA4cNtzX6cuDA6hH/amwegFl1HO3MP/nuhHsqUfFfgqnftpkA3CUbbLJPLcbuHlX9LWnHzOvzJuxG6jkEcW7oS2P7RuAW/fSvXKs9g4XLa3bMbzzz5LGFAwOEM4cx98Hbt9z52/r57KNXIfr+EAdvy4bQBtRlBvo3yOiSwCeA/CXRPTXs1a4VOnokRAl5MddssgtKgNoeBgYHsnDsm2MJg1ESr7KEI95FzXpt+1bvuna9Ds1r+hvVm1jK26grrL6+4CHLaqVer7Ktc9OYJMRuLci1FZEor+ZVnS+3kK294ga4S9ckWwfXmGOfqvDFvfIhxbTe7PFAGTcLZ1Z3zUc8dhTNqt8kgWby09IAB5ZObKv01QAivIC+jKALxdRNpYuEjpjNdqfCrnC3vKI+MAXrorfzk5oefaR3eb7PpChdW30EdoaJtZrUPsWL9NntTc14YmUyQvIt2zXiWDqdTOfo2kJoDE51Wuah8ulLfKpDy36dV7wyXMyYgADfY0Q6eMTjfuJPC3XpjQJxq9LADmogOS16oU1C+i+WEDbNwjRXG38HcrKyKrHNeQln9u4d6nUvBvcQJ955VxWRPR2DrzYALGIwFl7aVsYQBMSQFZI8VxXn/jaAFwg6w/P9xUGkIvLZYsSgHzx0Y32qKNZ4bPgkDp9VcpS+0Die2RQn+qPc5MAtBc/8iTwxNYmM2sO3WcDKJWAXm2grFwKrFwmwuW6TvXR0dcLbH7Efv5qs9ix2W4ASgtVkRdck1lWySMPI1mqgVmgRa2subyli4TdQJcaE/Q1N4E3rpt4j6hR51wZQLMSQPR3eHk89HkryEJLdB42gPiCw2Vgt2ZvUwHlJAHo71V6UD/6dZbQfQzAhu0bgHUPxyPv+XS8tSmbZJpBRZnkgXhHbmaF1hQck2+aSmeR5qu8fYNwqbOp3TxIsEdUjf9si1MzUbJO8r56bVMZpOVtuk59r/FOXe2VqwooA1zSYh7wybOnRxx4o0ppNVdvyKJeNEzUQOPcgTyrPEuqH4nAACTKJf/AZO2GLEvaK1RDspRQBhyr0VxocOQtmZCJAXzb08lOvHCB8GnOTIOqh/fwxIFyHkCeuzR91E/NSmTNSgAmN9BOUAG1hQF4pNm7Q5zAd+tO457sAyYf/Uz1037LMC7jE01k1lkIDMAXs8oAokH48Apg+ZK4i9iqlWIl7buJpmkaHPV1SQB5rEJNNPicqYAcVUA+sBqHM+TRrA0gJgGk0JMFLbuBtk6CM38b+nrFv9t3G/dk/zRuaMvA4E0LoP5eYGwikdVcQ/cZgecCXP7BPWXBFFw6zTzgYwOYzTAinqtbTlw0CS/fcy19/Z3ZUAEZbAB5RN5sdSNY4bOhUr4rDlDT9Ysw0N/8Js8OQmAAvihCBZTtpZxpUK8zqIDahawqoPZYA+xlx2wAmTJJXtrOH7DQULcB5CkBzIYdox3wLb4JF+MYVGNz1jpLla7L93+W2jGogNJQDxswi2U2pUtuIw163s369beCsl8FZ3VNZnUtbHHy3LhWrOZl/CfXezEJoCAjcN4hSVqBcbXfLFEORtzKMY1b1wEPLcvuFNEGBAkgDXLH4Ix/tL+WkWUQtm2l4KECcnpZ5IzZVgH5wOYdlSkPw/uVHuFe7Kqz0QaQoxG4kzaCZYGpeBdTyKrqkzCds+2Lcrn9NjxPBAaQhi2PCHEvSxTLVpGlQ0nPpbwnPNegpg5SAWkoTAUENLf6zWF1WvhGMBXtYACZ+pmnBOBDpsJkE2jXQe19kWrp4Zz2UKQgqIDSsHihPUhau5BFAli5FHgwmi26oQ9cdr26ETjfIp2wTixxItqyE9iGXCSAFo3AJS0WUKvIYn/QaWk3PG0iyXvGhOl51U/8MjEAVQJIz8oblR5xit8sITCATkSWAbV2WAz8vFcMneYF5IlibQASGdqlWRdKxVsn31hALebRDmawdJFwf15nOl7Eo/ymvYAc3zFmwM3bAWP21GiBAXQi0o6ijKUl/4M7MtHgUgF1LgPILRicD/QJNy8bgNd7BiNwUaEgVLRrJ/C29Z5pLe/73NNRhAQwywg2gE5EO6N8+oKsP4pxA7VBI2FWVUD6znHZTJlU1i5dmwPKRF14KAgVeW4EbAZ5egG59hHEbAAdMF6bRJAAOhFFe1IAMPqn1393rgRQA8RW/fUZT51qBkQiSNzElLyRPQ/byW+p7zUkgCmw8CzRw4M0A2pRAugA18YEfKUCHa7+rUoAnbBgaxKBAQSY4bQBRJPEbLqBeoIBceLTbGHRECBDzTQzaTbrTqiogGYAcQZxHpA7z5s9oCSP3citYFF00BORMoE3aQPwVQF5HS7TmQgMIMAMl266g43As+uapKGZhWAlY/RZiVZ19TYsWgDs2ZF+dnCnYnBAeNGcOCsCKQIWtZCPGtNx4l+pBOzaYj9ZcI4gMIAAM5oNBtcOZDBuFsuSWlQBZSqqCXdN33ybmfyf2NbwYS8aPvszWjUCAyIm1xxHYACdhGd3NQ6tLxyOAZKHt4kvPvok5oyRrU7mLLChZvz12wlTyOUikba/wkdv71IhzRMEBtBJGOjPx5CXB1zeHLNp9Mq447JQCaAuGc1iWfN3bsoPTbuBOlRA8wTBDTTAjA6IVDjnMJvt0i4bwHxB2v6Kjoi3VTwCAwgwQz+HQIUcEM16iuQJzQ7RiWbptqBdNoB5g5Qd1kECAFAQAyCiXyKiE0T0NhF9mYiWFEFHgANpk/uOTcDTO2aHlrmCZifjZoyJc9j3fNbR6k7gecwBipIAvgpgFzM/AeA9AF8oiI4AG9IMvSuXNc5G7SAUawNo8r2dm4GPPZ2xrPk7KeWCVBWQz9Tn2Ak8T1AIA2Dmv2FmGb7yVQBri6AjwIG50uk7QQ1VRwvxc7J6VnXwbuzOQMq3CCogAABxwR2IiP4CwL9j5t+3PH8BwAsAMDw8vOfAgQNNlTMyMoKhoaGm6ZyLaLXO+0i8e5BH8iIpd5QArEEF69CLChG+OnIdlQX5bGLKWv/lKONxGgAz40WM5kKDDWtRwRbqwyWewlsjt0Pf1rAVfVhDYoeu6fstQglP06D1OQBsRx9WUQUneQJXkXO49SbQynjev3//YWbeq99vGwMgor8FYIrf+kVm/vMozRcB7AXw/exByN69e/nQoUNN0XPw4EHs27evqXfnKlqu84tRWz+f6Dedh1eOAFPTeJlH8ZF9z+eTZ9b637oLHD2d7Z1mcekD4MxFYPVDOHj5/dC3dZw6D1y5Ia5N3+LBKPDGcftzALhyHTh1Adi9HViysCV680Ar45mIjAygbfsAmPk7Ugj6HIDvBvB3fCb/gIAABXUVRhg6RqR5SfmogFatFBP/bJ4GOMsoZCMYEX0CwD8D8DwzjxVBQ4AHnnwUmJ4umoq5g9m0m4T53w+2T+LjRUU0ryd/oLidwL8OoA/AV0kMmleZ+R8WREuADYvnkF758a3A1RuYupyjvWL1ymzpCzGcBw7gRCsSQBegEAbAzFuKKDdgHmNoENi6Hrh8Nr88t3qeQlUIZjHsxFxEqgoo7IEFwk7ggID8UIQKKCAFloYKG+kABAYQEJAfZnVOCfsAvGD7JkEFBCAwgICAHBEkgI5BmgooSAAAAgMICMgPszmnyHDdRR/C3ukIRmAnQu8JCMgLszmprFgKbFwDbApRVIwg7W/ieWAAQDgQJiAgP8yqEZiAdatmr7w5hxw2gnUBggQQEBAw/1CXAMJE70JgAAEBeSFMNh2E8C18EBhAQEBeCHNO5yEwZScCAwgIyA1hsukYhE/hhcAAAgLyQlhtdhDCmck+CAwgICAvhLmmcxC+hRcCAwgIyA1h1uk4pH2SoXxOj5urCPsAAgLyQpj/OwdS9eM6a/nje2aHlg5GYAABAXkh6Js7D65vEr5XUAEFBOSGMJ90HlwSQEBgAAEB+SFwgI6BjwooIDCAgIDcEFQKnYcQ9tmJwAACAvJCmGs6B/JbBAnAidA6AQG5IXCAjoE8KC1IAE4EBhAQkBfCXNM5kEdlBgnAidA6AQF5IdgAOge1mvgbJAAnCmEARPQ/EdHbRPQWEf0NEa0ugo6AgIB5ilqQAHxQVOv8EjM/wcxPAvgKgJ8tiI6AgPwQJIDOQV0CCAzAhUJah5nvKz8XoGGyCQgICGgdUgIITNmJwkJBENGXAPwIgHsA9hdFR0BA7li8sGgKAoIE4AVibs/im4j+FsDDhkdfZOY/V9J9AUA/M/8PlnxeAPACAAwPD+85cOBAU/SMjIxgaGioqXfnKkKdZx99IEyDUZvFMouucxFIq/Oj6MPDVMFxnsAHmJlFytqHVr7z/v37DzPzXv1+2xiAL4hoPYC/ZOZdaWn37t3Lhw4daqqcgwcPYt++fU29O1cR6twdCHU24NgZ4OYd4LFNwEPLZo2udqKV70xERgZQlBfQVuXn9wI4UQQdAQEB8xRS9V8OKiAXirIB/AIRbQdQA3AewD8siI6AgID5iC3rgP4+YNnioinpaBTCAJj57xdRbkBAQJegtwJsWls0FR2PIB8FBAQEdCkCAwgICAjoUgQGEBAQENClCAwgICAgoEsRGEBAQEBAlyIwgICAgIAuRWAAAQEBAV2KwAACAgICuhSFxwLKAiK6AbFzuBmsAHAzR3LmAkKduwOhzt2BVuq8nplX6jfnFANoBUR0yBQMaT4j1Lk7EOrcHWhHnYMKKCAgIKBLERhAQEBAQJeimxjAbxZNQAEIde4OhDp3B3Kvc9fYAAICAgIC4ugmCSAgICAgQEFgAAEBAQFdiq5gAET0CSI6SUSniejzRdOTF4jot4noOhEdVe4tI6KvEtGp6O9S5dkXojY4SUTfVQzVzYOIHiGirxHRcSI6RkQ/Gd2fz3XuJ6LXiOhIVOd/Ht2ft3WWIKIyEb1JRF+Jfs/rOhPROSJ6h4jeIqJD0b321pmZ5/U/AGUAZwBsAtAL4AiAHUXTlVPdPg7gaQBHlXu/CODz0fXnAfyr6HpHVPc+ABujNikXXYeM9V0F4OnoeiGA96J6zec6E4Ch6LoC4FsAPjyf66zU/Z8A+LcAvhL9ntd1BnAOwArtXlvr3A0SwLMATjPz+8w8BeAAgO8rmKZcwMxfB3Bbu/19AH43uv5dAH9XuX+AmSeZ+SyA0xBtM2fAzFeZ+Y3o+gGA4wDWYH7XmZl5JPpZif4x5nGdAYCI1gL4TwH8n8rteV1nC9pa525gAGsAXFR+X4ruzVcMM/NVQEyYAB6K7s+rdiCiDQCeglgRz+s6R6qQtwBcB/BVZp73dQbwbwD8DICacm++15kB/A0RHSaiF6J7ba1zIYfCzzLIcK8bfV/nTTsQ0RCAPwXwj5n5PpGpaiKp4d6cqzMzVwE8SURLAHyZiHY5ks/5OhPRdwO4zsyHiWifzyuGe3OqzhE+ysxXiOghAF8lohOOtLnUuRskgEsAHlF+rwVwpSBaZgMfENEqAIj+Xo/uz4t2IKIKxOT/B8z8Z9HteV1nCWa+C+AggE9gftf5owC+l4jOQahsv52Ifh/zu85g5ivR3+sAvgyh0mlrnbuBAbwOYCsRbSSiXgCfAfDvC6apnfj3AD4XXX8OwJ8r9z9DRH1EtBHAVgCvFUBf0yCx1P8tAMeZ+ZeVR/O5ziujlT+IaADAdwA4gXlcZ2b+AjOvZeYNEOP1PzLzD2Me15mIFhDRQnkN4DsBHEW761y05XuWrOufgvAYOQPgi0XTk2O9/hDAVQDTECuCHwOwHMD/B+BU9HeZkv6LURucBPDJoulvor7fBiHmvg3grejfp+Z5nZ8A8GZU56MAfja6P2/rrNV/HxpeQPO2zhBeikeif8fkPNXuOodQEAEBAQFdim5QAQUEBAQEGBAYQEBAQECXIjCAgICAgC5FYAABAQEBXYrAAAICAgK6FIEBBARYQERLiOi/iq5XE9GfFE1TQECeCG6gAQEWRPGGvsLMrtALAQFzFt0QCyggoFn8AoDNUSC2UwAeY+ZdRPSjEFEZywB2AfjXEKHGPwtgEsCnmPk2EW0G8BsAVgIYA/APmNkV3yUgYFYRVEABAXZ8HsAZZn4SwE9rz3YB+EGIeC1fAjDGzE8BeAXAj0RpfhPAP2LmPQB+CsD/MhtEBwT4IkgAAQHN4WssziR4QET3APxFdP8dAE9EEUs/AuCPlWilfbNPZkCAHYEBBAQ0h0nluqb8rkGMqxKAu5H0EBDQkQgqoIAAOx5AHD2ZGcx8H8BZIvo0ICKZEtHuPIkLCGgVgQEEBFjAzLcAfJOIjgL4pSay+CEAP0ZEMsLjvDiKNGD+ILiBBgQEBHQpggQQEBAQ0KUIDCAgICCgSxEYQEBAQECXIjCAgICAgC5FYAABAQEBXYrAAAICAgK6FIEBBAQEBHQp/n/qakEGAMmixwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# simulate an ARMA(0,1)=MA(1) noise process (moving average of order 1)\n",
-    "mean2 = 0 \n",
-    "sigma2 = 1\n",
-    "theta = 0.7\n",
-    "m = 501\n",
-    "Fs = 1 # sampling rate\n",
-    "\n",
-    "sigma_e = np.sqrt(sigma2**2/(1+theta**2))\n",
-    "yt2 = np.zeros(m) # make an array with zeros\n",
-    "et = np.zeros(m) # make an array with zeros\n",
-    "# initialization of the first entry\n",
-    "et[0] = np.random.randn() * sigma2\n",
-    "yt2[0] = et[0]\n",
-    "# generate values of yt using the MA(1)\n",
-    "for i in range(1, m):\n",
-    "    et[i] = np.random.randn() * sigma_e\n",
-    "    yt2[i] = theta * et[i-1] + et[i]\n",
-    "\n",
-    "# plot the time series\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(time, yt2, color='pink')\n",
-    "plt.title('MA(1) time series')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('time')\n",
-    "\n",
-    "# plot ACF MA(1) process\n",
-    "ACF = plot_acf(yt2, lags=None, alpha=0.05, title='ACF of MA(1)', color='pink')\n",
-    "plt.grid()\n",
-    "\n",
-    "# plot the MA(1) PSD\n",
-    "F, PSD = signal.periodogram(yt2, fs=Fs, scaling='density', return_onesided=False)\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(F, PSD, color='pink')\n",
-    "plt.title('PSD of MA(1)')\n",
-    "plt.ylabel('Power: PSD')\n",
-    "plt.xlabel('Frequency')\n",
-    "# The PSD values seem to have larger values at lower frequencies. this indicates that lower frequencies \n",
-    "# have higher contribution to data variability (moving avergare reduces the high frequency noise) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b335e79",
-   "metadata": {},
-   "source": [
-    "### Exercise 5. Time series modelling (Video 5)\n",
-    "**Introduction:** In this exercise, you will focus on the Best Linear Unbiased Estimation (BLUE). With BLUE, if the components of the time series are known, you can use the linear model of observations to estimate these components. \n",
-    "\n",
-    "**Exercise:** In this excercise, you calculate the BLUE estimates. First, create your matrix $A$ and $\\Sigma_{Y}$ which need to have dimensions of 501x5 (501: rows and 5 columns) and 501x501 respectively. Can you explain what these 5 parameters are? For $\\Sigma_{Y}$, you can use the np.eye function from numpy. Having defined these two matrices, we can obtain the BLUE estimats of \n",
-    "\n",
-    "$$\n",
-    "\\hat{X}=(A^T \\Sigma_{Y}^{-1}A)^{-1}A^T \\Sigma_{Y}^{-1}Y,\\, \\, \\hat{Y}=...,\\, \\, \\hat{\\epsilon}=...\n",
-    "$$ \n",
-    "\n",
-    "along with their covariance matrices $\\Sigma_{\\hat{X}}=(A^T \\Sigma_{Y}^{-1}A)^{-1}$, $\\Sigma_{\\hat{Y}}=...$ and $\\Sigma_{\\hat{\\epsilon}}=...$. \n",
-    "\n",
-    "After you have estimated the $\\hat{X}$ (having 5 elements), we you can compare each element of the $\\hat{x}$ with the corresponding values from the original time series you simulated ($y_0$, $r$, $A_m$, $\\phi$, $o_k$). The precision of the parameters can also be obtained from $\\Sigma_{\\hat{X}}$. You may also want to follow hypothesis tests to test the statistical significance of the estimated parameters. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "f478f83b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "y0: True value is: 1 ,  Estimated value is: 1.1104794855280757\n",
-      "r: True value is: 0.02 ,  Estimated value is: 0.019749730013193766\n",
-      "Am: True value is: 1 ,  Estimated value is: 0.8989701446874958\n",
-      "phi0: True value is: 0.6283185307179586 ,  Estimated value is: 0.5656440688725001\n",
-      "Ok: True value is: 5 ,  Estimated value is: 4.972202425807366\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# create your code here:\n",
-    "# copied from Exercise 1\n",
-    "m = 501\n",
-    "time = np.arange(501) \n",
-    "y_0 = 1 \n",
-    "r = 0.02 \n",
-    "omega = 2 * np.pi/100 \n",
-    "Am = 1 \n",
-    "phi_0 = 0.2*np.pi \n",
-    "t_k = 300 \n",
-    "sigma = 1\n",
-    "\n",
-    "# for your observations you can make use of y4 (in Exercise 1)\n",
-    "y = y4\n",
-    "# or make a new observation vector based on y3 (signal in Exercise 1) and yt (correlated noise in Exercise 4)\n",
-    "y = y3+yt2\n",
-    "\n",
-    "# the design matrix A based on linear regression + seasonality:\n",
-    "A = np.stack((np.ones(m), time, np.cos(omega*time), np.sin(omega*time)), axis=1)\n",
-    "# include column due to offset into A\n",
-    "u = np.zeros(m)\n",
-    "u[t_k:] = 1\n",
-    "A = np.column_stack((A,u))\n",
-    "# make the covariance matrix\n",
-    "Qyy = (sigma**2) * np.eye(m) \n",
-    "\n",
-    "# implement BLUE Equations:\n",
-    "xhat = np.linalg.inv(A.T @ np.linalg.inv(Qyy) @ A) @ A.T @ np.linalg.inv(Qyy) @ y #BLUE: BLUE of x: xhat\n",
-    "yhat = A @ xhat #BLUE of $Y$: yhat\n",
-    "ehat = y - yhat #BLUE of e: ehat\n",
-    "# covariance matrix of xhat\n",
-    "Qxhat = np.linalg.inv(A.T @ np.linalg.inv(Qyy) @ A)\n",
-    "\n",
-    "# Comparisons of xhat with the initial (true) values x:\n",
-    "y_0_hat = xhat[0] # compare with y_0\n",
-    "print('y0: True value is:', y_0,',  Estimated value is:', y_0_hat)\n",
-    "r_hat = xhat[1]   # compare with r\n",
-    "print('r: True value is:', r,',  Estimated value is:', r_hat)\n",
-    "\n",
-    "Am_hat = np.sqrt(xhat[2]**2 + xhat[3]**2) # compare with Am\n",
-    "print('Am: True value is:', Am,',  Estimated value is:', Am_hat)\n",
-    "\n",
-    "phi_0_hat = np.arctan(xhat[2]/xhat[3]) # compare with phi_0\n",
-    "print('phi0: True value is:', phi_0,',  Estimated value is:', phi_0_hat)\n",
-    "\n",
-    "O_k_hat = xhat[4]  # compare with O_k\n",
-    "print('Ok: True value is:', O_k,',  Estimated value is:', O_k_hat)\n",
-    "\n",
-    "plt.figure(figsize=(10, 5))\n",
-    "plt.grid()\n",
-    "plt.plot(time, y, label='Original $Y$ (with noise)', color='pink')\n",
-    "plt.plot(time, yhat, label='Estimated $Y$: yhat', color='r')\n",
-    "plt.plot(time, y3, label='True $Y$ (without noise)', linestyle='--', color='b')\n",
-    "plt.title('Time series modelling')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('time')\n",
-    "plt.legend()\n",
-    "\n",
-    "# we now check the residuals to identify the noise structure of ehat. Is it similar to the generated noise? \n",
-    "ACF = plot_acf(ehat, lags=None, alpha=0.05, title='ACF of ehat', color='pink')\n",
-    "plt.grid()\n",
-    "# try to make a new observation vector based on y3 (Exercise 1) and yt (Exercise 4): $Y$ = y3+yt2, and compare the results.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "120c7cb7",
-   "metadata": {},
-   "source": [
-    "### Exercise 6. Time series forecasting (Video 6) - optional\n",
-    "\n",
-    "So far, the $Y$ values are provided for time instants from $t=0$ ($Y(0)$) to $t=500$ ($Y(500)$). The current question pertains to predicting values for $Y$ in the subsequent epochs, such as $Y_p[501]=$? In order to answer this question, we should note that similar to $Y$, $Y_p$ also consists of two components: deterministic (functional) part and stochastic part. For example, for epoch 501, you can make a new design matrix (for now only one row) to calculate the functional component: \n",
-    "\n",
-    "$$\n",
-    "A_p= [1, 501, \\cos(501\\omega), \\sin(501\\omega), 1]\n",
-    "$$\n",
-    "\n",
-    "and \n",
-    "\n",
-    "$$\n",
-    "Y_{p_F} = A_p \\hat{X}\n",
-    "$$\n",
-    "\n",
-    "You need to calculate its stochastic component $Y_{p_S}$. That component depends on the noise process. If the noise process is white noise, there is no contribution from that component. If the noise is MA(1) the stochastic part of the prediction can be obtained from\n",
-    "\n",
-    "$$\n",
-    "Y_{p_S}=Y_{501} = \\theta \\epsilon_{500}+\\epsilon_{501}\n",
-    "$$\n",
-    "\n",
-    "where the error $\\epsilon_{501}$ in epoch 501 is not known, so the best prediction for that would be $\\epsilon_{501}=0$. The error $\\epsilon_{500}$ can be determined from the time series data $\\hat{e}$, and therefore $Y_{501} = \\theta \\epsilon_{500}$. This is not further the subject of discussion/elaboration in this week. In the project, we will provide a Python function to handle/estimate the AR(p) parameters. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "b96e5dd1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Predicted value for epoch 501 is: [16.76202718]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\AAMIRI~1\\AppData\\Local\\Temp/ipykernel_27972/3448084877.py:29: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
-      "  plt.plot(np.array([500,501]), np.array([y[500], yp]), label='Estimated y: yhat', color='r')\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x2019f166ee0>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from statsmodels.tsa.arima.model import ARIMA\n",
-    "from statsmodels.tsa.arima_process import ArmaProcess\n",
-    "\n",
-    "Ap  = np.array([1, 501, np.cos(omega*501), np.sin(omega*501), 1])\n",
-    "yp1 = Ap@xhat \n",
-    "\n",
-    "plt.figure(figsize=(10, 5))\n",
-    "plt.grid()\n",
-    "plt.plot(time, ehat, label='Least squares residuals', color='pink')\n",
-    "plt.title('Time series modelling')\n",
-    "plt.ylabel('ehat(t)')\n",
-    "plt.xlabel('time')\n",
-    "plt.legend()\n",
-    "\n",
-    "# if you select $Y$ = y3+yt2, containing MA(1)\n",
-    "model = ARIMA(ehat, order=(0, 0, 1))\n",
-    "result = model.fit()\n",
-    "# Forecast future values\n",
-    "forecast_steps = 1\n",
-    "yp2 = result.forecast(steps=forecast_steps)\n",
-    "yp = yp1+yp2\n",
-    "\n",
-    "print('Predicted value for epoch 501 is:', yp)\n",
-    "\n",
-    "plt.figure(figsize=(10, 5))\n",
-    "plt.grid()\n",
-    "plt.plot(time, yp, label='Original $Y$ (with noise)', color='b')\n",
-    "#plt.plot([500,501], [$Y$(500), yp], label='Estimated $Y$: yhat', color='r')\n",
-    "plt.plot(np.array([500,501]), np.array([yp[500], yp]), label='Estimated $Y$: yhat', color='r')\n",
-    "plt.title('Time series modelling')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('time')\n",
-    "plt.legend()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/book/time_series/optional.md b/book/time_series/optional.md
deleted file mode 100644
index a9eb47fc30e8cb55913d556db387ea9f34a52bcb..0000000000000000000000000000000000000000
--- a/book/time_series/optional.md
+++ /dev/null
@@ -1,161 +0,0 @@
-(optional)=
-# Supplementary material
-
-```{admonition} MUDE Exam Information
-:class: tip, dropdown
-The material on this page is provided to give you extra insight into time series analysis and how it is used in practice. This material is not part of the exam.
-```
-
-## Partial ACF
-
-A partial ACF (PACF) is a covariance between an observation in a time series with observations at prior time steps with the relationships of intervening observations removed. We work this out using a simple example.
-
-:::{card} Illustration of PACF
-
-Let us assume that we have an autoregressive noise process
-
-$$Y_t = \beta Y_{t-1}+\epsilon_t, \hspace{30px} 0\leq\beta<1, \hspace{30px} t=2,...,m$$
-
-where $\epsilon_t$ is an i.i.d noise process (e.g. distributed as $\epsilon_t\sim N(0,\sigma^2)$). Multiple applications of the above *autoregressive* formula give
-
-$$\begin{align*}
-Y_t&=Y_t\\ 
-Y_{t+1} &= \beta Y_t + \epsilon_{t+1}\\ 
-Y_{t+2}&=\beta Y_{t+1} + \epsilon_{t+2} = \beta^2 Y_t + \beta \epsilon_{t+1} + \epsilon_{t+2}\\ &\vdots \end{align*}$$
-
-We can show that the covariance between $Y_t$ and $Y_{t+1}$ is
-
-$$
-Cov(Y_t, Y_{t+1})  = \sigma^2\beta
-$$
-
-We can also show that the covariance between $Y_t$ and $Y_{t+2}$ is
-
-$$
-Cov(Y_t, Y_{t+2}) =  \sigma^2\beta^2
-$$
-
-Hence, the $Y_t$ and $Y_{t+2}$ are correlated, even though according to the expression $Y_t = \beta Y_{t-1}+\epsilon_t$ should just depend on the previous value. This makes sense, since the previous value again depends on its own previous value, et cetera. However, using the partial ACF allows to 'remove' this correlation.
-
-### Worked example (optional)
-
-Let us now take a look into a worked example on PACF to remove the intervening term, $\beta Y_{t+1}$ between $Y_t$ and $Y_{t+2}$. As we saw before, ACF can be obtained from
-
-$$
-\text{COV} = Cov(Y_t, Y_{t+2}) =  \sigma^2\beta^2
-$$
-
-Regarding the partial ACF, it is knowns from the autoregression $Y_t = \beta Y_{t-1}$ that $\hat{Y}_t = \hat{Y}_{t+2} = \beta Y_{t+1}$. Therefore:
-
-$$
-\begin{align*}
-\text{PCOV} &= Cov(Y_t-\hat{Y}_t,Y_{t+2}-\hat{Y}_{t+2})\\&=Cov(Y_t-\beta Y_{t+1},Y_{t+2}-\beta Y_{t+1})
-\\ & = \mathbb{E}((Y_t-\beta Y_{t+1})(Y_{t+2}-\beta Y_{t+1}))\\
-& = \mathbb{E}(Y_tY_{t+2}-\beta Y_t Y_{t+1}-\beta Y_{t+1}Y_{t+2} + \beta^2Y_{t+1}^2)\\
-& = \sigma^2\beta^2-\beta\sigma^2\beta-\beta\sigma^2\beta+\sigma^2\beta^2=0
-\end{align*}
-$$
-
-This shows indeed that with the partial ACF the correlation for a time lag of 2 (or higher) becomes zero.
-
-#### Normalized ACF vs Partial ACF (optional)
-
-The figure shows a simulated example of $Y_t = 0.8Y_{t-1}+\epsilon_t$ having 1000 samples. The spectrum of the normalized ACF clearly shows that there is a (decreasing) correlation up till lag 10, while the spectrum of the partial ACF only shows correlation at lag 1.
-
-![acf_pacf](./figs/acf_pacf.png "acf_pacf")
-
-:::
-
-## Identifying orders of ARMA process
-
-In time series analysis, [ACF and PACF plots](ACF) can be used to provide the model orders: The value of $p$ for AR and the value of $q$ for MA, and hence to select the best model for forecasting.
-
-Here we assume the time series is stationary. We can then plot the ACF and PACF to identify the orders of AR and MA (numbers $p$ and $q$ of coefficients $\beta_i$ and $\theta_i$) in the ARMA model.
-
-We first look for a gradual diminishing pattern (tail-off pattern) in either ACF or PACF. The following guidelines can then be used to interpret ACF and PACF plots:
-
-* If the **tail-off** pattern is at ACF, then AR (and not MA) is an appropriate model. The cut-off at PACF will then provide order $p$ for AR($p$);
-* If the **tail-off** pattern is at PACF, then MA (and not AR) is an appropriate model. The cut-off at ACF will then provide order $q$ for MA($q$);
-* If the **tail-off** pattern is at both ACF and PACF, then the stochastic process cannot be expressed just as AR or MA. The appropriate model is then ARMA.
-* Test data is usually required to validate the selected orders $p$ and $q$.
-
-### Example: identification of AR(1)
-
-The tail-off pattern is at ACF. AR has a cut-off at PACF at lag 1. The best model is then AR(1) = ARMA($p=1,q=0$)
-
-![pacf_acf](./figs/pacf_acf.png "pacf_acf")
-
-### Example: identification of MA(1)
-
-The tail-off pattern is at PACF. MA has a cut-off at ACF at lag 1. The best model is then MA(1) = ARMA($p=0,q=1$)
-
-![pacf_acf_2](./figs/pacf_acf_2.png "pacf_acf_2")
-
-
-## Testing stationarity
-
-Different tests can be performed to test whether or not a time series is stationary. One of the commonly used methods is the **Augmented Dickey-Fuller (ADF)** test. **ADF** is also optional material. 
-
-Consider a time series
-
-$$Y_t = \beta Y_{t-1}+\epsilon_t$$
-
-where we see that the value at time $t$ depends on the previous value at time $t-1$ plus the noise $\epsilon_t$ (this is an autoregressive process, as we will see later in the section [ARMA process](ARMA)). This implies that if $\beta=1$, the noise is **accumulated** and thus the process is **not stationary**. It is known to be a so-called *random walk noise* process. 
-
-Single differencing gives
-
-$$
-\begin{align*}
-\Delta Y_t = Y_t - Y_{t-1} &= \beta Y_{t-1}+\epsilon_t-Y_{t-1}\\
-&= (\beta - 1)Y_{t-1}+\epsilon_t \\&= \gamma Y_{t-1} + \epsilon_t
-\end{align*}
-$$
-
-The parameter $\gamma = \beta-1$ plays an important role to test the stationarity of the time series.
-
-### ADF test
-
-The ADF test is performed using the following two hypotheses:
-
-* **Null Hypothesis ($\mathcal{H}_0$)**: Time series is non-stationary ($\gamma=0\implies\beta=1$)
-* **Alternative Hypothesis ($\mathcal{H}_a$)**: Time series is stationary ($\gamma<0\implies\beta<1$)
-
-The null hypothesis assumes that the time series consists of non-stationary noise, mainly **Random Walk** noise. Under the alternative hypothesis, the Random Walk noise is absent, and therefore the time series is stationary.
-
-The test statistic is (which can be tested in a given confidence level) given by:
-
-$$
-T_{\text{ADF}}=\frac{\hat{\gamma}}{\sigma_{\hat{\gamma}}}
-$$
-
-The test statistic, $T_{ADF}$ is a **negative number**. The more negative it is, the stronger the rejection of the hypothesis, and hence the more level of confidence that the series is a stationary process.
-
-(BLUP)=
-## Best Linear Unbiased Prediction (BLUP)
-
-Best Linear Unbiased Prediction (BLUP) is the equivalent of Best Linear Unbiased Estimation (BLUE). BLUE can be applied to estimate *deterministic* values, while BLUP is applied in case some of the parameters are of a *random* nature. In case of forecasting, this is indeed the case if the underlying noise process is not white noise.
-
-Consider the (augmented) linear model of observation equations as
-
-$$\begin{bmatrix}Y \\ Y_p\end{bmatrix}=\begin{bmatrix}\mathrm{A} \\ \mathrm{A}_p \end{bmatrix}x+\begin{bmatrix}\epsilon \\ \epsilon_p \end{bmatrix}, \hspace{10px}\mathbb{D}\left(\begin{array}{c}Y\\ Y_p\end{array}\right)=\begin{bmatrix}\Sigma_{Y} & \Sigma_{YY_p} \\\Sigma_{Y_p Y} & \Sigma_{Y_p} \end{bmatrix}$$
-
-The best linear unbiased estimation, **BLUE**, of $x$ is:
-
-$$\hat{X}=(\mathrm{A}^T\Sigma_{Y}^{-1}\mathrm{A})^{-1}\mathrm{A}^T\Sigma_{Y}^{-1}Y,\hspace{10px}\Sigma_{\hat{X}}=(\mathrm{A}^T\Sigma_{Y}^{-1}\mathrm{A})^{-1}$$
-
-Without derivation, we now give the best linear unbiased prediction, **BLUP**, of $Y_p$:
-
-$$\hat{Y_p}=\mathrm{A}_p\hat{X}+\Sigma_{Y_p Y}\Sigma_{Y}^{-1}\hat{\epsilon}= \hat{Y}_F + \hat{S}$$
-
-with the covariance matrix
-
-$$\Sigma_{\hat{Y_p}}=\mathrm{A}_p\Sigma_{\hat{X}}\mathrm{A}_p^T+\Sigma_{Y_p Y}\Sigma_{Y}^{-1}\Sigma_{\hat{\epsilon}}\Sigma_{Y}^{-1}\Sigma_{YY_p}$$
-
-*Two processes play a role in prediction:*
-* $\hat{Y}_F = \mathrm{A}_p\hat{X}$ is the deterministic part modelling the functional effects (such as trend and seasonality).
-* $\hat{S}= \Sigma_{Y_p Y}\Sigma_{Y}^{-1}\hat{\epsilon}$ is the stochastic part (stochastic process).
-
-
-```{note}
-For a purely random process (white noise), we have $\Sigma_{Y_p Y}=0$ and, therefore, the stochastic process/part will NOT affect the prediction.
-```
diff --git a/book/time_series/proof.pdf b/book/time_series/proof.pdf
deleted file mode 100644
index 6fb80f8830cd5110313aa71e0eeeeea9ec9a8599..0000000000000000000000000000000000000000
Binary files a/book/time_series/proof.pdf and /dev/null differ
diff --git a/book/time_series/stationarity.md b/book/time_series/stationarity.md
index ab8975d45ac18500f1f9d40453b4b733865b3b29..e0cc6ac46dac1cd7bd7c1252b8a77883f70a85a6 100644
--- a/book/time_series/stationarity.md
+++ b/book/time_series/stationarity.md
@@ -1,8 +1,9 @@
+(stationary)=
 # Time Series Stationarity
 
 
 ```{admonition} Definition
-A stationary time series is a stochastic process whose statistical properties do not depend on the time at which it is observed.
+A stationary time series $S(t)$ is a stochastic process whose statistical properties do not depend on the time at which it is observed.
 ```
 
 This means that parameters such as *mean* and *(co)variance* should remain constant over time and not follow any trend, seasonality or irregularity.
@@ -23,6 +24,7 @@ $$
 Var(S_t)=\mathbb{E}((S_t-\mu)^2)=c_0=\sigma^2
 $$
 
+Notice that we have introduced the new notation $S_t$ to denote a stationary time series. The time series $Y_t$ is then a non-stationary time series.
 ## Why stationary time series?
 
 Stationarity is important if we want to use a time series for forecasting (predicting future behaviour), which is not possible if the statistical properties change over time.
@@ -32,202 +34,11 @@ In practice, we may in fact be interested in for instance the trend and seasonal
 (stationarize)=
 ## How to "stationarize" a time series?
 
-In general, there are five ways to make a non-stationary time series to a stationary one. They are known as transformation methods. An important requirement is that such transformation is regular, or admissible, meaning that a back-transformation is possible. 
-
-Common methods are:
-
-* Difference transformation of data
-* Moving average of data
-* Least-squares fit (detrending)
-
-### Single and double differencing
-
-Single differencing of $Y=[Y_1,...,Y_m]^T$ makes a time series $\Delta Y_t=Y_t - Y_{t-1},\; t\geq 2$ with starting value of $\Delta Y_1 = Y_1$. This is a **regular transformation** of data, and hence allowed, as shown in the equation below.
-
-(SD)=
-$$
-\begin{bmatrix}
-    \Delta Y_1 \\ \Delta Y_2 \\ \Delta Y_3 \\ \vdots \\ \Delta Y_m
-\end{bmatrix} = 
-\begin{bmatrix}
-    1 & 0 &   & \dots & 0\\
-    -1 & 1 & 0 &   &  \\
-    0 & -1 & 1 & \ddots & \\
-    \vdots & \ddots &\ddots & \ddots & 0 \\
-    0 & \dots & 0 & -1 & 1
-\end{bmatrix}
-\begin{bmatrix}
-    Y_1\\ Y_2\\ Y_3\\ \vdots\\ Y_m
-\end{bmatrix}\Longleftrightarrow
-\begin{bmatrix}
-    Y_1\\ Y_2\\ Y_3\\ \vdots \\Y_m
-\end{bmatrix} = 
-\begin{bmatrix}
-    1 & 0 & 0 & \dots & 0\\
-    1 & 1 & 0 & \dots & 0\\
-    1 & 1 & 1 &  & \vdots\\
-    \vdots & \vdots & \vdots & \ddots &0\\
-    1 & 1 & 1 & 1 & 1
-\end{bmatrix}
-\begin{bmatrix}
-    \Delta Y_1 \\ \Delta Y_2 \\ \Delta Y_3 \\ \vdots \\ \Delta Y_m
-\end{bmatrix}
-$$
-
-#### Worked example
-
-Let us consider the following time series
-
-$$
-Y_t = y_0+rt+\epsilon_t
-$$
-
-This time series is non-stationary, due to the presence of a linear trend (the expectation is a function of $t$).
-
-Now we will apply single differencing to the time series:
-
-$$
-\begin{align*}
-\Delta Y_t = Y_t-Y_{t-1} &= y_0+rt+\epsilon_t-(y_0+r(t-1)+\epsilon_{t-1}) \\
-&= r+\Delta \epsilon_t
-\end{align*}
-$$
-
-It follows that $\mathbb{E}(\Delta Y_t)=r$, and therefore not a function of $t$ anymore.
-
-Now consider
-
-$$
-Y_t = y_0+rt+at^2+\epsilon_t
-$$
-
-We again apply single differencing:
-
-$$
-\begin{align*}
-\Delta Y_t = Y_t-Y_{t-1} &= y_0+rt+at^2+\epsilon_t-(y_0+r(t-1)+a(t-1)^2+\epsilon_{t-1}) \\
-&= r-a+2at+\Delta \epsilon_t
-\end{align*}
-$$
-
-In this case we find that $\mathbb{E}(\Delta Y_t)=r-a+2at$, which is still a function of $t$ and therefore not stationary. The solution would be to continue the process, which is referred to as *double differencing*:
-
-$$
-\Delta^2 Y_t = \Delta Y_t - \Delta Y_{t-1}
-$$
-
-:::{card} Exercise
-
-Show for yourself that applying double differencing to the time series $Y_t = y_0+rt+at^2+\epsilon_t$ results in a stationary time series $\Delta^2 Y_t$.
-
-````{admonition} Solution
-:class: tip, dropdown
-
-$$
-\begin{align*}
-\Delta^2 Y_t = \Delta Y_t-\Delta Y_{t-1} &= r-a+2at+\Delta \epsilon_t-(r-a+2a(t-1)+\Delta \epsilon_{t-1}) \\&= 2a+\Delta^2\epsilon_t
-\end{align*}
-$$
-
-with $\mathbb{E}(\Delta^2 Y_t)=2a$.
-
-```{figure} ./figs/doubledifference.png 
----
-height: 300px
-name: doubledifference
----
-Original time series (second-order polynomial) on the left; double differenced time series on the right.
-```
-````
-:::
-
-### Moving average
-
-The moving average of $Y = [Y_1, ..., Y_m]^T$ will create a time series $\bar{Y}_t = {\bar{Y}_1,...,\bar{Y}_m}$, with 
-
-$$
-\bar{Y}_t = \frac{1}{k}\sum_{i=1}^{k}Y_{t-i}
-$$
-
-where the length of the interval over which the average is taken is equal to $k$ (hence, the moving average only uses past values up till $k-1$ epochs ago).
-
-The difference between two time series provides a (nearly) stationary time series $\Delta Y_t = Y_t - \bar{Y}_t$.
-
-```{figure} ./figs/moving_avg.png
----
-height: 300px
-name: moving_avg
----
-Original time series and moving average on the right; stationarized times series on the left.
-```
-:::{card} Exercise
-
-Consider a random process time series as:
-
-$$
-Y_t
-= U \cos (\theta t) + V \sin (\theta t)
-$$
-
-where $U$ and $V$ are two uncorrelated random variables with zero means, and unit variances and $\theta$ is a deterministic value in the interval $\theta \in [-\pi, \pi]$. Show that this noise process is stationary.
-
-```{admonition} Solution
-:class: tip, dropdown
-
-Because $\mathbb{E}(U)=\mathbb{E}(V)=0$, it simply follows that 
-
-$$
-\mathbb{E}(Y_t)=0
-$$
-
-For a given $\tau$, the covariance between $Y_t$ and $Y_{t+\tau}$ is obtained as:
-
-$$
-\begin{align*}
-c_\tau = Cov(Y_t, Y_{t+\tau})
-&= \mathbb{E}(Y_tY_{t+\tau}) - \mathbb{E}(Y_t)\mathbb{E}(Y_{t+\tau})
-= \mathbb{E}(Y_t Y_{t+\tau})\\
-&= \mathbb{E}
-\biggl(
-    \bigl[ U \cos(\theta t) + V \sin(\theta t) \bigr]
-    \bigl[ U \cos(\theta t + \theta \tau)
-         + V \sin(\theta t + \theta \tau) \bigr]
-\biggr)
-\end{align*}
-$$
-
-The multiplication consists of four terms in which the terms $U^2$, $V^2$, $UV$ and $VU$ appear. Because the two random variables $U$ and $V$ are uncorrelated with zero means and unit variances, it follows that:
-
-$$
-\mathbb{E}(U^2) = \mathbb{E}(V^2) = 1
-\quad \mathrm{and} \quad
-\mathbb{E}(UV) = \mathbb{E}(VU) = 0
-$$
-
-This, with the previous equations, gives:
-
-$$
-Cov(Y_t, Y_{t+\tau})
-= \cos(\theta t) \cos(\theta t + \theta \tau)
-+ \sin(\theta t) \sin(\theta t + \theta \tau)
-$$
-
-Using the identity $\cos(a-b)=\cos(a)\cos(b)+\sin(a)\sin(b)$, it follows:
-
-$$
-Cov(Y_t, Y_{t+\tau})
-= \cos(\theta t + \theta \tau - \theta t)
-= \cos(\theta \tau)
-$$
-
-Which is a function of $\tau$, but not a function of time $t$. This shows that the random process is stationary.
-
-```
-:::
+There are several ways to make a time series stationary. In this course we will focus on detrending the data using least-squares fit.
 
 ### Least-squares fit
 
-If we can express the time series $Y=[Y_1, ..., Y_m]^T$ with a linear model of observation equations as $Y = \mathrm{Ax} + \epsilon$, we can apply [best linear unbiased estimation](BLUE) (equivalent to weighted least-squares) to estimate the parameters $\mathrm{x}$ that describe e.g. the trend and seasonality:
+If we can express the time series $Y=[Y_1, ..., Y_m]^T$ with a linear model of observation equations as $Y = \mathrm{Ax} + \epsilon$, we can apply [best linear unbiased estimation](BLUE) to estimate the parameters $\mathrm{x}$ that describe e.g. the trend and seasonality:
 
 $$
 \hat{X}=(\mathrm{A}^T\Sigma_{Y}^{-1}\mathrm{A})^{-1}\mathrm{A}^T\Sigma_{Y}^{-1}Y 
@@ -271,16 +82,43 @@ $$
 
 The time series of residuals (left panel) is indeed a stationary time series.
 
+:::{card} Question Stationary Time Series
+
+Which of the four options is a stationary time series?
+
+```{figure} ./figs/stat_question.png
+---
+height: 300px
+name: stationary_example
+---
+Example of a stationary time series.
+```
+
+````{admonition} Solution
+:class: tip, dropdown
+
+The time series in the second panel is stationary. The mean and variance are constant over time.
+````
+:::
+
+
+
+### Other ways to make a time series stationary
+When model specification is not straightforward, other methods can be used to make a time series stationary. Two common methods are single differencing and moving average. Single differencing of $Y=[Y_1,...,Y_m]^T$ makes a time series $\Delta Y_t=Y_t - Y_{t-1}$. Another way to create an (almost) stationary time series is by taking the moving average of the time series. Where we apply a moving average of $k$ observations to the time series $Y$ to create a new time series $\bar{Y}_t = \frac{1}{k}\sum_{i=1}^{k}Y_{t-i}$, and then take the difference between the original time series and the moving average to obtain a (nearly) stationary time series $\Delta Y_t = Y_t - \bar{Y}_t$.
+
+Both these methods do not require a model specification. So in cases where the model is not known, these methods can be used to make the time series stationary.
+
+
 ## ... and then what?
 
-We have seen different ways of obtaining a stationary time series from the original time series. The reason is that in order to make predictions (forecasting future values) we need to account for both the **signal-of-interest** and the **noise**. [Estimating the signal-of-interest](modelling_tsa) was covered in the previous section. In the next sections we will show how the noise can be modelled as a stochastic process. Given a time series $Y=\mathrm{Ax}+\epsilon$, the workflow is as follows:
+We have seen different ways of obtaining a stationary time series from the original time series. The reason is that in order to make predictions (forecasting future values, beyond the time of the last observation in the time series) we need to account for both the **signal-of-interest** and the **noise**. [Estimating the signal-of-interest](modelling_tsa) was covered in the previous section. In the next sections we will show how the noise can be modelled as a stochastic process. Given a time series $Y=\mathrm{Ax}+\epsilon$, the workflow is as follows:
 
 1. Estimate the signal-of-interest $\hat{X}=(\mathrm{A}^T\Sigma_{Y}^{-1}\mathrm{A})^{-1}\mathrm{A}^T\Sigma_{Y}^{-1}Y$ (Section [Modelling and estimation](modelling_tsa)).
 
-2. Model the noise using the Autoregressive Moving Average (ARMA) model, using the stationary time series $S:=\hat{\epsilon}=Y-\mathrm{A}\hat{X}$ (Section [ARMA](ARMA)).
+2. Model the noise using the Autoregressive (AR) model, using the stationary time series $S:=\hat{\epsilon}=Y-\mathrm{A}\hat{X}$ (Section [AR](AR)).
 
 3. Predict the signal-of-interest: $\hat{Y}_{signal}=\mathrm{A}_p\hat{X}$, where $\mathrm{A}_p$ is the design matrix describing the functional relationship between the future values $Y_p$ and $\mathrm{x}$ (Section [Forecasting](forecast)).
 
-4. Predict the noise $\hat{\epsilon}_p$ based on the ARMA model.
+4. Predict the noise $\hat{\epsilon}_p$ based on the AR model.
 
 5. Predict future values of the time series: $\hat{Y}_p=\mathrm{A}_p\hat{X}+\hat{\epsilon}_p$ (Section [Forecasting](forecast)).
diff --git a/book/time_series/videos.md b/book/time_series/videos.md
deleted file mode 100644
index 81a1a9f21dbd6892c1742823436963689cf0ff73..0000000000000000000000000000000000000000
--- a/book/time_series/videos.md
+++ /dev/null
@@ -1,94 +0,0 @@
-# Supplementary Videos
-
-**MMMMM:** _these videos are from 2022; decide which videos to keep for 2023, and (optionally) integrate them into the respective pages. Update text and dropdown as needed._
-
-_Codes for the videos:_  
-
-```bash
-2.5.1a, components of TS: https://youtu.be/_euBY6-CK54
-2.5.1a, worked example: https://youtu.be/8kqQiI4ni68
-2.5.1b, stationary TS: https://youtu.be/MOTNftkPvbw
-2.5.1b, worked example: https://youtu.be/hZSqlXjUg0k
-2.5.1c, autocov fxn: https://youtu.be/wdlGzV5zr-w
-2.5.1c, worked example (1 of 2, 4:46 long): https://youtu.be/SoCKuF2eiTc
-2.5.1c, worked example (2 of 2, 4:11 long): https://youtu.be/n-pkyuL95JA
-2.5.1d, ARMA process: https://youtu.be/v7odPKK_eN4
-2.5.2a, TSA model and est: https://youtu.be/6iJ1kOnl7is
-2.5.2, TS forecasting: https://youtu.be/0KEX-Z_lCTM
-```
-
-The story of this chapter is told once more in this section with a series of videos. 
-
-```{admonition} MUDE exam information 
-:class: tip, dropdown
-These videos overlap to some extent with the theory presented in the book, and are meant to provide additional perspective on the same topic. Additional material that is presented in these videos is _not_ part of the exam; in other words, the exam scope is limited  to contents that appear in the previous pages.
-```
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/_euBY6-CK54" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/8kqQiI4ni68" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/MOTNftkPvbw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/hZSqlXjUg0k" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/wdlGzV5zr-w" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/SoCKuF2eiTc" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/n-pkyuL95JA" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/v7odPKK_eN4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/6iJ1kOnl7is" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/0KEX-Z_lCTM" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
diff --git a/book/time_series/y_values.csv b/book/time_series/y_values.csv
new file mode 100644
index 0000000000000000000000000000000000000000..23ec73383f20059fa387e5d827ec2d591fcf743f
--- /dev/null
+++ b/book/time_series/y_values.csv
@@ -0,0 +1,500 @@
+1.145597157270158029e+01
+1.594789763966840113e+01
+1.230148270704014024e+01
+9.133581406881100762e+00
+2.581845617730080811e+00
+1.095456963568298825e+01
+3.698093178200730691e+00
+1.144636423959347482e+01
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