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

Automatic trend changepoint detection

Trend changepoints are locations in your time series where the trend component of the model suddenly changes its slope. There could be many reasons why these changepoints occur, depending upon your dataset. For example, Facebook developed Prophet to forecast their own business problems; they may be modeling the number of daily active users and see a sudden change of trend upon the release of a new feature.

Airline passenger numbers may suddenly change as economies of scale allow much cheaper flights. The trend of carbon dioxide in the atmosphere was relatively flat for tens of thousands of years, but then suddenly changed during the Industrial Revolution.

From our work with the Divvy dataset in previous chapters, we saw a slow-down of growth after approximately two years. Let's take a closer look at this example to learn about automatic changepoint detection.

Default changepoint detection

Prophet sets changepoints by first specifying...