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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

Correcting outliers that cause wide uncertainty intervals

In the first type of outlier we looked at, the problem was that the seasonality was affected and forever changed yhat in the forecast (if you remember from Chapter 2, Getting Started with Facebook Prophet, yhat is the predicted value for future dates contained in Prophet's forecast DataFrame). In this second problem, yhat is minimally affected but the uncertainty intervals widen dramatically.

To simulate this issue, we need to modify our NatGeo data a bit. Let's say that Instagram introduced a bug in their code that capped likes to 100,000 per post. It somehow went unnoticed for a year before being fixed, but unfortunately, all likes above 100,000 were lost. Such an error would look like this:

Figure 9.6 – Capped likes on National Geographic's Instagram account

You can simulate this new dataset yourself with the following code:

df3 = df.copy()
df3.loc[df3['ds'].dt...
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