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  • Forecasting: Principles and Practice, the Pythonic Way by Rob J. Hyndman book cover
  • Forecasting: Principles and Practice, the Pythonic Way by Rob J. Hyndman book cover
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Forecasting: Principles and Practice, the Pythonic Way

by Rob J. Hyndman

Forecasting methods through Python

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AlbakiReads Editorial

Forecasting methods through Python

This textbook introduces forecasting across a range of planning horizons, from near-term call volumes and telecommunications traffic to longer-term demand for power and inventory needs. It explains methods with the aim of helping readers apply them sensibly, using Python examples and datasets drawn from the authors’ consulting experience. This edition adds chapters on neural networks and foundation forecasting models, extending its coverage of approaches to prediction. The practical examples connect the methods to operational and business planning contexts.

Good fit for readers who enjoy

  • Readers learning forecasting methods with Python
  • Students and practitioners interested in applying forecasts to planning and operations
  • Readers seeking coverage of neural networks and foundation forecasting models

Themes

Forecasting methodsPythonPlanning and operationsNeural networksFoundation forecasting models

Based on publisher information and book metadata.

Book overview

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/

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