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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

Chapter 4: Seasonality

One quality that sets time series apart from other datasets is that very often—but not always—the data has a certain rhythm to it. That rhythm may be yearly, possibly due to the Earth's rotation around the Sun, or daily, if rooted in the Earth's rotation around its axis. The tidal cycle follows the Moon's rotation around the Earth.

Traffic congestion follows the human activity cycle throughout the day and the five-day workweek followed by the two-day weekend; financial activity follows the quarterly business cycle. Your own body follows cycles due to your heartbeat, breathing rate, and circadian rhythm. On the very small physical and very short temporal scales, the vibration of atoms is a cause of periodicity in data. Prophet calls these cycles seasonalities.

In this chapter, you will learn about all the different types of seasonalities Prophet fits by default, how to add new ones, and how to control them. In particular, we...

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