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 took the lessons learned from the basic Mauna Loa model you built in Chapter 2, Getting Started with Facebook Prophet, and learned what changes you need to make when the period of your data is not daily. Specifically, you used the Air Passengers dataset to model monthly data and used the freq argument when making your future DataFrame in order to hold back Prophet from predicting daily.

Then, you used the hourly data from Divvy's bike share program to set the future frequency to hourly so that Prophet would increase the granularity of its prediction timescale. Finally, you simulated periodic missing data in the Divvy dataset and learned a different way to match the future DataFrame's schedule to that of the training data, in order to prevent Prophet from making unconstrained predictions.

Now that you know how to handle the different datasets you will encounter in this book, you're ready for the next topic! In the next chapter, you...