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Forecasting Time Series Data with Prophet

You're reading from   Forecasting Time Series Data with Prophet Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool

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Product type Paperback
Published in Mar 2023
Publisher Packt
ISBN-13 9781837630417
Length 282 pages
Edition 2nd Edition
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Author (1):
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Greg Rafferty Greg Rafferty
Author Profile Icon Greg Rafferty
Greg Rafferty
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Table of Contents (20) Chapters Close

Preface 1. Part 1: Getting Started with Prophet
2. Chapter 1: The History and Development of Time Series Forecasting FREE CHAPTER 3. Chapter 2: Getting Started with Prophet 4. Chapter 3: How Prophet Works 5. Part 2: Seasonality, Tuning, and Advanced Features
6. Chapter 4: Handling Non-Daily Data 7. Chapter 5: Working with Seasonality 8. Chapter 6: Forecasting Holiday Effects 9. Chapter 7: Controlling Growth Modes 10. Chapter 8: Influencing Trend Changepoints 11. Chapter 9: Including Additional Regressors 12. Chapter 10: Accounting for Outliers and Special Events 13. Chapter 11: Managing Uncertainty Intervals 14. Part 3: Diagnostics and Evaluation
15. Chapter 12: Performing Cross-Validation 16. Chapter 13: Evaluating Performance Metrics 17. Chapter 14: Productionalizing Prophet 18. Index 19. 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 will begin just as we have learned to do with our other models in this book, by importing the necessary libraries, loading our data,...

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