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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

Adding default country holidays

Prophet uses the Python holidays package to populate a default list of holidays by country and, optionally, by state or province. To specify which region to build a holiday list for, Prophet requires the name or ISO code of that country. A complete list of all countries available, with their ISO codes, and also any states or provinces that can be included, can be viewed in the package's README file here: https://github.com/dr-prodigy/python-holidays#available-countries.

To add the default holidays, Prophet includes an add_country_holidays method, which simply takes the ISO code for that country. Let's walk through an example using the Divvy dataset again, first adding holidays for the United States, and then including a few additional holidays specific to Illinois, as Divvy is located in Chicago.

We begin just as we have learned to do with our other models in this book, by importing the necessary libraries, loading our data, and instantiating...

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