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

Chapter 7: Trend Changepoints

During the development of Prophet, the engineering team recognized that real-world time series will frequently exhibit abrupt changes in their trajectories. As a fundamentally linear regression model, Prophet would not be capable of capturing these changes without special care being taken. You may have noticed in the previous chapters, however, that when we plotted the forecast components in our examples, the trend line was not always perfectly straight. Clearly, the Prophet team has developed a way for Prophet to capture these bends in the linear model. The locations of these bends are called changepoints.

Prophet will automatically identify these changepoints and allow the trend to adapt appropriately. However, there are several tools you can use to control this behavior if Prophet is underfitting or overfitting these rate changes. In this chapter, we'll look at Prophet's automatic changepoint detection to provide you with an understanding...