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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
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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

Chapter 6: Growth Modes

So far in this book, every forecast we've built followed only one growth mode: linear. The trend sometimes had some small bends where the slope either increased or decreased, but fundamentally the trend consisted of linear segments. However, Prophet features two additional growth modes: logistic and flat.

Modeling your time series with a growth mode that is not optimal can often fit the actual data very well. But, as you'll see in this chapter, even if the fit is realistic, the future forecast can become wildly unrealistic. Sometimes the shape of the data will inform which growth mode to choose and sometimes you'll need domain knowledge and a bit of common sense. This chapter will help guide you to an appropriate selection. Furthermore, you will learn when and how to apply these different growth modes. Specifically, this chapter will cover the following:

  • Applying linear growth
  • Understanding the logistic function
  • Saturating forecasts...
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