Search icon
Subscription
0
Cart icon
Close icon
You have no products in your basket yet
Save more on your purchases!
Savings automatically calculated. No voucher code required
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
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

Who this book is for

This book is for anyone who wants to use Facebook's Prophet to improve their forecasts. Data scientists and data analysts, machine learning engineers and software engineers, and even business managers will benefit from the topics covered in this book. All that is required is that the reader is comfortable with working in either Python or R, or is willing to learn how. The business manager who is familiar with Python can follow the examples included in this book and will learn how to modify them to fit their own use cases; the data scientist will gain a more technical understanding of what Prophet is doing under the hood and how it works. However, this book is intended mostly as a how-to guide. It will not provide a fully rigorous explanation of the math and statistics that underpin the equations controlling Prophet. For that, I suggest reading the original Prophet paper: Taylor SJ, Letham B. 2017. Forecasting at scale. PeerJ Preprints 5:e3190v2 (https://doi.org/10.7287/peerj.preprints.3190v2).

lock icon The rest of the chapter is locked
Next Chapter arrow right
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 $15.99/month. Cancel anytime}