Forecasting: Principles and Practice, the Pythonic Way

Additional authors: Azul Garza, Cristian Challu, Max Mergenthaler, Kin G Olivares

Forecasting is required in many situations. Deciding whether to build another power generation plant in the next five years requires forecasts of future demand. Scheduling staff in a call centre next week requires forecasts of call volumes. Stocking an inventory requires forecasts of stock requirements. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning.

This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use Python with many data sets taken from the authors' own consulting experience.

In this Python edition, two new chapters have been added on neural networks and foundation forecasting models. The latest version of the book is freely available online at https: //OTexts.com/fpppy/

Professor of Statistics

Professor