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 3: Non-Daily Data

When Prophet was first released, it assumed all data would be on a daily scale, with one row of data per day. It has since grown to handle many different granularities of data, but because of its historical conventions, there are few things to be cautious of when working with non-daily data.

In this chapter, you will look at monthly data (and in fact, any data that is measured in timeframes greater than a day) and see how to change the frequency of predictions to avoid unexpected results. You will also look at hourly data and observe an additional component in the components plot. Finally, you will learn how to handle data that has regular gaps along the time axis.

This chapter will cover the following:

  • Using monthly data
  • Using sub-daily data
  • Using data with regular gaps