Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800568532
Length 270 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Greg Rafferty Greg Rafferty
Author Profile Icon Greg Rafferty
Greg Rafferty
Arrow right icon
View More author details
Toc

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 seasonality swings

We'll be using a new dataset in this chapter to look at outliers: the average number of likes per day of posts on National Geographic's Instagram account, @NatGeo. This data was collected on November 21, 2019.

I've chosen this dataset because it exhibits several significant outliers, which are marked in the following plot:

Figure 9.1 – Outliers on National Geographic's Instagram account

Each dashed vertical line indicates a moment where the time series deviated significantly. The second line from the left indicates a radical trend change in the summer of 2015 but the other four lines indicate outliers, with the last two outliers spanning across wide time ranges. We'll specifically be looking at the line occurring in mid-2016, in August to be precise. This represents the most extreme outliers. The 2014 set of outliers can be safely ignored, as they do not affect the forecast...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime