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 default state/province holidays

Adding the holidays specific to Illinois is not so straightforward, because the add_country_holidays method only takes an argument for country, but not state or province. To add state- or province-level holidays, we need to use a new Prophet function, make_holidays_df. Let's import it here:

from fbprophet.make_holidays import make_holidays_df

This function takes as input a list of years for which to populate the holidays as well as arguments for the country and state or province. Note that you must use all years in your training DataFrame as well as all years you intend to predict on. That is why, in the following code, we build a year list to contain all unique years in the training DataFrame. Then, because our make_future_dataframe command will add one year to the forecast, we need to extend that year list to include one additional year:

year_list = df['ds'].dt.year.unique().tolist()
# Identify the final year, as an integer...