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Forecasting Time Series Data with Facebook Prophet

You're reading from   Forecasting Time Series Data with Facebook Prophet Build, improve, and optimize time series forecasting models using the advanced forecasting tool

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800568532
Length 270 pages
Edition 1st Edition
Languages
Tools
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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 (18) Chapters Close

Preface 1. Section 1: Getting Started
2. Chapter 1: The History and Development of Time Series Forecasting FREE CHAPTER 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

Summary

Outliers are a fact of any data analysis, but they do not always have to cause headaches. Prophet is very robust at handling most outliers without any special consideration, but sometimes problems can arise. In this chapter, you learned about the two problems most common with outliers in Prophet: uncontrolled seasonality and exploding uncertainty intervals.

In both cases, simply removing the data is the best approach to solving the problem. As long as data exists in other periods of the seasonality cycles for those gaps where data was removed, Prophet has no problem finding a good fit.

You also learned several automated outlier detection techniques, from the basic techniques of Winsorization and trimming, which tend not to work well on time series exhibiting a trend, to the more advanced technique of stacking forecasts and using errors in the first model to remove outliers for the second model.

Finally, you learned how to model outliers as special events, which has...

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