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Forecasting: Principles and Practice, the Pythonic Way
Preface
1
Getting started
2
Time series graphics
3
Time series decomposition
4
Time series features
5
The forecaster’s toolbox
6
Judgmental forecasts
7
Time series regression models
8
Exponential smoothing
9
ARIMA models
10
Dynamic regression models
11
Forecasting hierarchical and grouped time series
12
Advanced forecasting methods
13
Some practical forecasting issues
14
Neural networks
15
Foundation forecasting models
Appendix: Using Python
Appendix: Data used in the book
Translations
About the authors
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Bibliography
Reviews
Review from Jim Hoover in Foresight, 2025:Q4
Reviews of R edition
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