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

Understanding additive versus multiplicative seasonality

In our Mauna Loa example in Chapter 2, Getting Started with Facebook Prophet, the yearly seasonality was constant at all values along the trend line. We added the values predicted by the seasonality curve to the values predicted by the trend curve to arrive at our forecast. There is an alternative mode of seasonality though, where we would multiply the trend curve by the seasonality. Take a look at this figure:

Figure 4.1 – Additive versus multiplicative seasonality

The upper curve demonstrates additive seasonality—the dashed lines that trace the bounds of the seasonality are parallel because the magnitude of seasonality does not change, only the trend does. In the lower curve though, these two dashed lines are not parallel. Where the trend is low, the spread caused by seasonality is low; but where the trend is high, the spread caused by seasonality is high. This can be modeled with multiplicative...