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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 conditional seasonalities

Suppose you work for a utility company in a college town and are tasked with forecasting the electricity usage for the coming year. The electricity usage is going to depend upon the population of the town to some extent, and as a college town, there are thousands of students who are only temporary residents! How do you set up Prophet to handle this scenario? Conditional seasonalities exist for this purpose.

Conditional seasonalities are those that are in effect for only a portion of the dates in the training and future DataFrames. A conditional seasonality must have a cycle that is shorter than the period in which it is active. So, for example, it wouldn't make sense to have a yearly seasonality that is active for just a few months.

Forecasting electricity usage in the college town would require you to set up either daily or weekly seasonalities—and possibly even both, depending upon the usage patterns, one daily/weekly seasonality...

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