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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

Modeling outliers as special events

There is one final way to work with outliers in Prophet; it's a technique we used with James Rodríguez's data in Chapter 7, Trend Changepoints—we can declare the outliers as a special event, essentially a holiday. By putting the outliers into the holidays DataFrame, we essentially instruct Prophet to apply trend and seasonality as if the data points were not outliers and capture the additional variation beyond trend and seasonality in the holiday term.

This could be useful if you know the extreme observations are due to some external factor that you do not expect to repeat. Such external factors could be the World Cup or a large marketing campaign but may also be mysterious and unknown. You could keep the data in your model but essentially disregard it. An added benefit is that you can simulate what would happen if the event repeats.

We'll again use the National Geographic data, but this time label that August 2016...

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