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

Modeling outliers as special events

There is one final way to work with outliers in Prophet; it's a technique we used with James Rodríguez's data in Chapter 7, Trend Changepoints—we can declare the outliers as a special event, essentially a holiday. By putting the outliers into the holidays DataFrame, we essentially instruct Prophet to apply trend and seasonality as if the data points were not outliers and capture the additional variation beyond trend and seasonality in the holiday term.

This could be useful if you know the extreme observations are due to some external factor that you do not expect to repeat. Such external factors could be the World Cup or a large marketing campaign but may also be mysterious and unknown. You could keep the data in your model but essentially disregard it. An added benefit is that you can simulate what would happen if the event repeats.

We'll again use the National Geographic data, but this time label that August 2016...