Forecasting: Principles and Practice, the Pythonic Way
About a year ago, we did a soft launch of the online edition of “Forecasting: Principles and Practice, the Pythonic Way” (FPPPY). In this latest edition of the FPP book, George Athanasopoulos and I are joined by four additional co-authors: Azul Garza, Cristian Challu, Max Mergenthaler and Kin Olivares. Our new co-authors are Python and forecasting experts, and have all been involved in developing the open-source Nixtla software that is used throughout the book.
Since the online edition was launched, we have made numerous small updates and improvements to the book, ironing out errors, improving the presentation, and simplifying the Python code where possible. Now it is available in print as well!
The first 13 chapters of the Python edition closely follow the corresponding R fpp3 edition, but with code examples in Python rather than R. We have also included two new chapters: Neural networks and Foundation forecasting models, which are dedicated to recent techniques and developments in neural networks applied to forecasting.
As with previous editions, all code and examples are fully reproducible, and exercises are provided for teaching or self-study. The online edition will remain free, and will be updated as needed. The print edition is available for purchase, and we hope it will be a useful resource for students, practitioners, and anyone interested in learning about forecasting with Python.