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

Hopefully, you experienced no issues installing Prophet on your machine at the beginning of this chapter. The potential challenge of getting the Stan dependency installed is greatly eased by using the Anaconda distribution of Python. After installation, we looked at the carbon dioxide levels measured in the atmosphere two miles above the Pacific Ocean, at Mauna Loa in Hawaii. We built our first Prophet model and, in just 12 lines of code, were able to forecast the next 10 years of carbon dioxide levels.

After that, we inspected the forecast DataFrame and saw the rich results that Prophet outputs. Finally, we plotted the components of the forecast - the trend, yearly seasonality, and weekly seasonality, to better understand the data's behavior.

There is a lot more to Prophet than just this simple example, though. The remainder of this book will be spent demonstrating all of the parameters and additional features available that allow you to have greater control over...