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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

Creating multi-day holidays

Sometimes, a holiday or other special event will span several days. Fortunately, Prophet includes functionality to handle these scenarios via the window arguments. The holidays DataFrame we have been building to populate our holidays in the previous examples can have the optional columns of 'lower_window' and 'upper_window'. These columns specify additional days either before or after the main holiday that Prophet will model.

For example, in the previous example, we modeled Christmas and Christmas Eve as two different holidays. Another method would have been just to model Christmas but include a 'lower_window' of 1, telling Prophet to include a single day before Christmas as part of the holiday. This assumes, of course, that Christmas Eve will always fall on the day before Christmas. If, however, Christmas Eve were a holiday that floated and did not always fall immediately before Christmas, this window method would not be...

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