These notes are a Python-centered read-along of the excellent Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos [1].
Please find the table of contents on Jupyter nbviewer.
[1] Hyndman, R.J., & Athanasopoulos, G. (2019) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3. Accessed on 2020-07-20.
I've long wanted to rework this for clarity and completeness (the book has been updated since 2020) as well as improved Python style. Unfortunately, while I got started at some point, I never followed all the way through on the rewrite.
In the meantime, I've occasionally been asked how the code can be run, or where to find the data. So now (2024-Sep) I'm posting a minimal update to make the notebooks easily runnable. Just follow these steps:
- Install uv.
- Install a copy of Python 3.8:
uv python install 3.8 - Set up a venv:
uv venv --python 3.8 - Install the dependencies:
uv pip install -r requirements.txt - Run Jupyter:
.venv/bin/jupyter-lab
Note: while I checked that the updated notebooks pass the smell test, I did not check them in detail for correctness. If you discover a problem or mistake, please file an issue.