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

Chapter 11: Cross-Validation

The concept of keeping training data and testing data separate is sacrosanct in machine learning and statistics. You should never train a model and test its performance on the same data. Setting data aside for testing purposes has a downside, though: that data has valuable information that you would want to include in training. Cross-validation is a technique that's used to circumvent this problem.

You may be familiar with k-fold cross-validation, but if you are not, we will briefly cover it in this chapter. K-fold, however, will not work on time series. It requires that the data be independent, an assumption that time series data does not hold. An understanding of k-fold will help you learn how forward-chaining cross-validation works and why it is necessary for time series data.

After learning how to perform cross-validation in Prophet, you will learn how to speed up the computing of cross-validation through Prophet's ability to parallelize...