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

Interpreting the regressor coefficients

Now let's look at how to inspect the effects of these additional regressors. Prophet includes a package called utilities, which has a function that will come in handy here, called regressor_coefficients. Let's import it now:

from fbprophet.utilities import regressor_coefficients

Using it is straightforward. Just pass the model as an argument and it will output a DataFrame with some helpful information about the extra regressors included in the model:

regressor_coefficients(model)

Let's take a look at this DataFrame:

Figure 8.6 – The regressor coefficients DataFrame

It features a row for each extra regressor in your model. In this case, we have one for temperature and three more for the weather conditions we included. The regressor_mode column naturally will have strings of either additive or multiplicative, depending upon the effect of each specific regressor on 'y'. The mean...