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

Adding conditional seasonalities

Suppose you work for a utility company in a college town and are tasked with forecasting the electricity usage for the coming year. The electricity usage is going to depend upon the population of the town to some extent, and as a college town, there are thousands of students who are only temporary residents! How do you set up Prophet to handle this scenario? Conditional seasonalities exist for this purpose.

Conditional seasonalities are those that are in effect for only a portion of the dates in the training and future DataFrames. A conditional seasonality must have a cycle that is shorter than the period in which it is active. So, for example, it wouldn't make sense to have a yearly seasonality that is active for just a few months.

Forecasting electricity usage in the college town would require you to set up either daily or weekly seasonalities—and possibly even both, depending upon the usage patterns, one daily/weekly seasonality...