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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

Interpreting the forecast DataFrame

Now, let's take a look at that forecast DataFrame by displaying the first three rows (I've transposed it here, in order to better see the column names on the page) and learn how these values were used in the preceding chart:

forecast.head(3).T

After running that command, you should see the following table print out:

Figure 2.4 – The forecast DataFrame

The following is a description of each of the columns in the forecast DataFrame:

  • 'ds': Datestamp or timestamp that values in that row pertain to
  • 'trend': Value of the trend component alone
  • 'yhat_lower': Lower bound of the uncertainty interval around the final prediction
  • 'yhat_upper': Upper bound of the uncertainty interval around the final prediction
  • 'trend_lower': Lower bound of the uncertainty interval around the trend component
  • 'trend_upper': Upper bound of the...
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