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

Creating custom holidays

The default holidays for the United States include both Thanksgiving and Christmas, as they are official holidays. However, it's quite plausible that Black Friday and Christmas Eve would also create ridership behavior that deviates from the expected trend. So, we naturally decide to include these in our forecast.

In this example, we will create a DataFrame of the default US holidays in a similar manner to how we created the DataFrame of the Illinois holidays previously, and then add our custom holidays to it. To create custom holidays, you simply need to create a DataFrame with two columns: holiday and ds. As done previously, it must include all occurrences of the holiday in the past (at least, as far back as your training data goes) and into the future that we intend to forecast.

In this example, we will start by creating the holidays DataFrame populated with the default US holidays and use the same year_list from the previous example:

holidays...