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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

Adding default state/province holidays

Adding the holidays specific to Illinois is not so straightforward, because the add_country_holidays method only takes an argument for country, but not state or province. To add state- or province-level holidays, we need to use a new Prophet function, make_holidays_df. Let's import it here:

from fbprophet.make_holidays import make_holidays_df

This function takes as input a list of years for which to populate the holidays as well as arguments for the country and state or province. Note that you must use all years in your training DataFrame as well as all years you intend to predict on. That is why, in the following code, we build a year list to contain all unique years in the training DataFrame. Then, because our make_future_dataframe command will add one year to the forecast, we need to extend that year list to include one additional year:

year_list = df['ds'].dt.year.unique().tolist()
# Identify the final year, as an integer...
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