Merge branch 'main' of gitlab.tudelft.nl:mude/2024-files

content/GA_2_4/README.md

+# README for Group Assignment 2.4
+
+*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.4, Friday, Dec 6, 2024.*
+
+The focus of this assignment is on time series analysis.
+
+Your primary objective is to complete all tasks in the notebook `Analysis.ipynb`. Unlike other weeks, for this GA it is not required to put your answers in a `Report.md` file (yay!). Make sure you use the space provided in the notebooks to complete the tasks, and that the output is included in the notebook when you commit and push it to GitHub.
+
+### Overview of material
+
+- this `README.md` with instructions
+- `Analysis.ipynb`, the Jupyter notebook with description and tasks, to be used for actual coding
+- `temperature.csv`, data file with acceleration measurements from the cantilever beam (tasks 7-9)
+
+You can complete this assignment with the same environment you used for WS 2.4 earlier this week (the only special package relative to other weeks is `statsmodels`).
+
+Grading and submission is identical to previous weeks. Note, however that the Tasks in the notebook that are very similar to WS 2.4 are not graded.
+
+## Task Overview
+
+As mentioned above: there is no `Report.md`, only a notebook!
+
+This assignment overlaps with WS 2.4 in terms of identifying time series components and removing them iteratively. Unlike WS 2.4, however, we will consider an offset. It may be possible for some group members to start reading and studying this part while the others are implementing the first part of the notebook.
+
+**End of file.**
+
+<span style="font-size: 75%">
+&copy; Copyright 2024 <a rel="MUDE" href="http://mude.citg.tudelft.nl/">MUDE</a>, TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">CC BY 4.0 License</a>.
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