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 learned that the models we built in the first few chapters of this book all featured linear growth. You learned that the logistic function was developed to model population growth and then learned how to implement this in Prophet by modeling the growth of the wolf population in Yellowstone after their reintroduction in 1995.

Logistic growth in Prophet can be modeled as either increasing up to a saturation limit called the cap or decreasing to a saturation limit called the floor. Finally, you learned how to model flat (or no growth) trends, where the trend is fixed to one value for the entire data period but seasonality is still allowed to vary. Throughout this chapter, you used the add_changepoints_to_plot function in order to overlay the trend line on your forecast plots.

Choosing the correct growth mode is important, and particularly so when forecasting further into the future. We looked at a couple of examples in this chapter where the incorrect...