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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

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).

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