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

Summary

In this chapter, you learned how to control the fit of the trend line by using changepoints. First, you used Divvy data to see how Prophet automatically selects potential changepoint locations and how you can control this by modifying the default number of potential changepoints and the changepoint range.

Then you learned a more robust way to control Prophet's changepoint selection through regularization. Just as with seasonality and holidays, changepoints are regularized by setting the prior scale. You then looked at the Instagram data of James Rodríguez and learned how to model the increase in likes per post he received both during and after the World Cups of 2014 and 2018. Finally, you learned how to blend these two techniques and enrich an automatically selected grid of potential changepoints with your custom changepoint locations.

In the next chapter, we will again look at the Divvy data, but this time we'll include the additional columns for temperature...