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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

Product type Paperback

Published in Mar 2021

Publisher Packt

ISBN-13 9781800568532

Length 270 pages

Edition 1st Edition

Languages

Python

Tools

Prophet

Concepts

Predictive Analytics

Author (1):

Greg Rafferty

View More author details

Table of Contents (18) Chapters

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

Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Hands-On Time Series Analysis with R

Rami Krispin

ISBN: 978-1-78862-915-7

Visualize time-series data and derive useful insights
Study auto-correlation and understand statistical techniques
Use time-series analysis tools from the stats, TSstudio, and forecast packages
Explore and identify seasonal and correlation patterns
Work with different time-series formats in R
Discover time-series models such as ARIMA, Holt-Winters, and more
Evaluate high-performance forecasting solutions

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Authors (1)

Greg Rafferty

Greg Rafferty

Greg Rafferty is a data scientist at Google in San Francisco, California. With over a decade of experience, he has worked with many of the top firms in tech, including Facebook (Meta) and IBM. Greg has been an instructor in business analytics on Coursera and has led face-to-face workshops with industry professionals in data science and analytics. With both an MBA and a degree in engineering, he is able to work across the spectrum of data science and communicate with both technical experts and non-technical consumers of data alike.

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