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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 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 on the population of the town to some extent, and as a college town, thousands of students 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 on the usage patterns, one daily/weekly seasonality for the summer...

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