Book Image

Forecasting Time Series Data with Facebook Prophet

By : Greg Rafferty
Book Image

Forecasting Time Series Data with Facebook Prophet

By: Greg Rafferty

Overview of this book

Prophet enables Python and R developers to build scalable time series forecasts. This book will help you to implement Prophet’s cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code. You will begin by exploring the evolution of time series forecasting, from the basic early models to the advanced models of the present day. The book will demonstrate how to install and set up Prophet on your machine and build your first model with only a few lines of code. You'll then cover advanced features such as visualizing your forecasts, adding holidays, seasonality, and trend changepoints, handling outliers, and more, along with understanding why and how to modify each of the default parameters. Later chapters will show you how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models and see some useful features when running Prophet in production environments. By the end of this Prophet book, you will be able to take a raw time series dataset and build advanced and accurate forecast models with concise, understandable, and repeatable code.
Table of Contents (18 chapters)
1
Section 1: Getting Started
4
Section 2: Seasonality, Tuning, and Advanced Features
13
Section 3: Diagnostics and Evaluation

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).