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 4: Seasonality

One quality that sets time series apart from other datasets is that very often—but not always—the data has a certain rhythm to it. That rhythm may be yearly, possibly due to the Earth's rotation around the Sun, or daily, if rooted in the Earth's rotation around its axis. The tidal cycle follows the Moon's rotation around the Earth.

Traffic congestion follows the human activity cycle throughout the day and the five-day workweek followed by the two-day weekend; financial activity follows the quarterly business cycle. Your own body follows cycles due to your heartbeat, breathing rate, and circadian rhythm. On the very small physical and very short temporal scales, the vibration of atoms is a cause of periodicity in data. Prophet calls these cycles seasonalities.

In this chapter, you will learn about all the different types of seasonalities Prophet fits by default, how to add new ones, and how to control them. In particular, we...