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

Adding binary regressors

The first thing to consider with additional regressors, whether binary or continuous, is that you must have known future values for your entire forecast period. This isn't a problem with holidays because we know exactly when each future holiday will occur. All future values must either be known, as with holidays, or must have been forecast themselves separately. You must be careful though when building a forecast using data that itself has been forecast: the error in the first forecast will compound the error in the second forecast, and the errors will continuously pile up.

If one variable is much easier to forecast than another, however, then this may be a case where these stacked forecasts do make sense. A hierarchical time series is an example case where this may be useful: you may find good results by forecasting the more reliable daily values of one time series, for instance, and using those values to forecast hourly values of another time series...