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

Tuning hyperparameters with grid search

For the final section in this chapter, we'll look at grid search and work through an example, continuing with this rainfall data. If you're not familiar with the concept of grid search, it's a way to exhaustively check all reasonable combinations of hyperparameters against a performance indicator and choose the best combination to train your final model. With Prophet, you might decide to select the following hyperparameters and values:

Figure 12.13 – Prophet grid search parameters

With these parameters, a grid search will iterate through each unique combination, use cross-validation to calculate and save a performance metric, and then output the set of parameter values which resulted in the best performance.

Prophet does not have a grid search method the way, for example, sklearn does. One is easy enough to build yourself in Python though, so let's see how to set it up. The first step is...