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Forecasting Time Series Data with Prophet

You're reading from Forecasting Time Series Data with Prophet Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool

Product type Paperback

Published in Mar 2023

Publisher Packt

ISBN-13 9781837630417

Length 282 pages

Edition 2nd Edition

Languages

Python

Tools

Prophet

Concepts

Data Science

Author (1):

Greg Rafferty

View More author details

Table of Contents (20) Chapters

Preface

1. Part 1: Getting Started with Prophet

2. Chapter 1: The History and Development of Time Series Forecasting FREE CHAPTER

3. Chapter 2: Getting Started with Prophet

4. Chapter 3: How Prophet Works

5. Part 2: Seasonality, Tuning, and Advanced Features

6. Chapter 4: Handling Non-Daily Data

7. Chapter 5: Working with Seasonality

8. Chapter 6: Forecasting Holiday Effects

9. Chapter 7: Controlling Growth Modes

10. Chapter 8: Influencing Trend Changepoints

11. Chapter 9: Including Additional Regressors

12. Chapter 10: Accounting for Outliers and Special Events

13. Chapter 11: Managing Uncertainty Intervals

14. Part 3: Diagnostics and Evaluation

15. Chapter 12: Performing Cross-Validation

16. Chapter 13: Evaluating Performance Metrics

17. Chapter 14: Productionalizing Prophet

18. Index

Why subscribe?

19. Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

additive regression model 10

additive seasonality

versus multiplicative seasonality 56-65

Anaconda 18

reference link 19

analyst-in-the-loop forecasting 30, 31

parameters 31

ARIMA 7, 8

automatic changepoint detection 132

Autoregressive Conditional Heteroscedasticity (ARCH) 8, 9

Autoregressive Network (AR-Net) 13

B

Bayesian sampling 193

binary columns 153

binary regressors

adding 152-156

Box-Jenkins model 7

C

cap 113

carrying capacity 34

ceiling 113

changepoints 131

automatic changepoint detection 132

custom changepoint locations, specifying 142-149

default changepoint detection 132-136

regularizing 136-141

clipping 173

components plots 26, 27

conda 18-20

conditional seasonalities

adding 74-78

conditional...

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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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