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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
Toc

Table of Contents (18) Chapters close

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 custom holidays

The default holidays for the United States include both Thanksgiving and Christmas, as they are official holidays. However, it's quite plausible that Black Friday and Christmas Eve would also create ridership behavior that deviates from the expected trend. So, we naturally decide to include these in our forecast.

In this example, we will create a DataFrame of the default US holidays in a similar manner to how we created the DataFrame of the Illinois holidays previously, and then add our custom holidays to it. To create custom holidays, you simply need to create a DataFrame with two columns: holiday and ds. As done previously, it must include all occurrences of the holiday in the past (at least, as far back as your training data goes) and into the future that we intend to forecast.

In this example, we will start by creating the holidays DataFrame populated with the default US holidays and use the same year_list from the previous example:

holidays...
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