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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 custom seasonalities

So far, the only seasonalities we have worked with are the defaults in Prophet: yearly, weekly, and daily. But there is no reason to limit ourselves to these seasonalities. If your data contains a cycle that is either longer or shorter than the 365.25-day yearly cycle, the 7-day weekly cycle, or the 1-day daily cycle, Prophet makes it easy to model this seasonality yourself.

A great example of a non-standard seasonality is the 11-year cycle of sunspots. Sunspots are regions on the Sun's surface that temporarily exhibit a much-reduced temperature, and hence appear much darker, than surrounding areas.

Beginning in approximately 1609, Galileo Galilei began systematic observation of sunspots and over the last 400+ years, this phenomenon has been constantly recorded. In fact, sunspots represent the longest continuously recorded time series of any natural phenomenon. Through these observations, scientists have identified a quasi-periodic cycle of 11...

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