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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

Technical requirements

The data files and code for examples in this chapter can be found at https://github.com/PacktPublishing/Forecasting-Time-Series-Data-with-Facebook-Prophet. In this chapter, we will walk through the process of installing many of the requirements. So, to begin this chapter, it is only necessary that you have a Windows, macOS, or Linux machine capable of running Anaconda with Python 3.x.

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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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