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

Seasonality truly is the heart of Facebook Prophet. This chapter covered a lot of ground; the foundations you learned here will be used throughout the remaining chapters of this book. Indeed, almost any model you build in Prophet will have seasonality considerations, whereas many of the upcoming chapters cover special cases that may or may not apply to your specific problem.

You started this chapter by learning the difference between additive and multiplicative seasonality, and how to identify whether your dataset features one or the other. We then briefly discussed Fourier series and demonstrated how a partial Fourier sum can build a very complex periodic curve. Using these ideas, you learned how setting the Fourier order of a seasonality can be used to control its shape by allowing either more or less freedom to bend along its path.

Next, you modeled the 11-year cycle of sunspots and learned how to add custom seasonalities. These custom seasonalities came into use...