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Forecasting Time Series Data with Facebook Prophet

You're reading from  Forecasting Time Series Data with Facebook Prophet

Product type Book
Published in Mar 2021
Publisher Packt
ISBN-13 9781800568532
Pages 270 pages
Edition 1st Edition
Languages
Author (1):
Greg Rafferty Greg Rafferty
Profile icon Greg Rafferty

Table of Contents (18) Chapters

Preface 1. Section 1: Getting Started
2. Chapter 1: The History and Development of Time Series Forecasting 3. Chapter 2: Getting Started with Facebook Prophet 4. Section 2: Seasonality, Tuning, and Advanced Features
5. Chapter 3: Non-Daily Data 6. Chapter 4: Seasonality 7. Chapter 5: Holidays 8. Chapter 6: Growth Modes 9. Chapter 7: Trend Changepoints 10. Chapter 8: Additional Regressors 11. Chapter 9: Outliers and Special Events 12. Chapter 10: Uncertainty Intervals 13. Section 3: Diagnostics and Evaluation
14. Chapter 11: Cross-Validation 15. Chapter 12: Performance Metrics 16. Chapter 13: Productionalizing Prophet 17. Other Books You May Enjoy

Chapter 8: Additional Regressors

In your first model in Chapter 2, Getting Started with Facebook Prophet, you forecasted carbon dioxide levels at Mauna Loa, using only the date, but no other information, to predict future values. Later, in Chapter 5, Holidays, you learned how to add holidays as additional information to further refine your predictions of bicycle ridership in the Divvy bike share network in Chicago.

The way holidays are implemented in Prophet is actually a special case of adding a binary regressor. In fact, Prophet includes a generalized method for adding any additional regressor, both binary and continuous.

In this chapter, you'll enrich your Divvy dataset with weather information by including it as an additional regressor. First, you will add binary weather conditions to describe the presence or absence of sun, clouds, or rain, and then next you will bring in continuous temperature measurements. Using additional regressors can allow you to include more...

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