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Python-centered read-along of Forecasting: Principles and Practice

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00-Preface-to-this-readalong.ipynb
00-Preface-to-this-readalong.ipynb
 
 
02-Time-series-graphics.ipynb
02-Time-series-graphics.ipynb
 
 
03-Time-series-decomposition.ipynb
03-Time-series-decomposition.ipynb
 
 
04-Time-series-features.ipynb
04-Time-series-features.ipynb
 
 
05-The-forecasters-toolbox.ipynb
05-The-forecasters-toolbox.ipynb
 
 
06-Judgmental-forecasts.ipynb
06-Judgmental-forecasts.ipynb
 
 
07-Time-series-regression-models.ipynb
07-Time-series-regression-models.ipynb
 
 
08-Exponential-smoothing.ipynb
08-Exponential-smoothing.ipynb
 
 
09-ARIMA-models.ipynb
09-ARIMA-models.ipynb
 
 
10-Dynamic-regression-models.ipynb
10-Dynamic-regression-models.ipynb
 
 
Contents.ipynb
Contents.ipynb
 
 
R-export-datasets.ipynb
R-export-datasets.ipynb
 
 
README.md
README.md
 
 
__init__.py
__init__.py
 
 
utils.py
utils.py
 
 

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fpp3-python-readalong

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.

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