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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

Table of Contents (18) Chapters

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

Performing k-fold cross-validation

We'll be using a new dataset in this chapter, the sales of an online retailer in the United Kingdom. This data has been anonymized, but it represents 3 years of daily sales amounts, as displayed in the following graph:

Figure 11.1 – Daily sales of an anonymous online retailer

This retailer has not seen dramatic growth over the 3 years of data, but it has seen a massive boost in sales at the end of the year. The main customer of this retailer is wholesalers, who typically make their purchases during the work week. This is why when we plot the components of Prophet's forecast, you'll see that Saturday and Sunday's sales are the lowest. We'll use this data to perform cross-validation in Prophet.

Before we get to modeling, though, let's first review traditional validation techniques to tune a model's hyperparameters and report performance. The most basic method is to take your full...

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