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Forecasting Time Series Data with Facebook Prophet

Forecasting Time Series Data with Facebook Prophet

By : Greg Rafferty
4.9 (17)
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Forecasting Time Series Data with Facebook Prophet

Forecasting Time Series Data with Facebook Prophet

4.9 (17)
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)
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1
Section 1: Getting Started
4
Section 2: Seasonality, Tuning, and Advanced Features
13
Section 3: Diagnostics and Evaluation

Chapter 8: Additional Regressors

In your first model in Chapter 2, Getting Started with Facebook Prophet, you forecasted carbon dioxide levels at Mauna Loa, using only the date, but no other information, to predict future values. Later, in Chapter 5, Holidays, you learned how to add holidays as additional information to further refine your predictions of bicycle ridership in the Divvy bike share network in Chicago.

The way holidays are implemented in Prophet is actually a special case of adding a binary regressor. In fact, Prophet includes a generalized method for adding any additional regressor, both binary and continuous.

In this chapter, you'll enrich your Divvy dataset with weather information by including it as an additional regressor. First, you will add binary weather conditions to describe the presence or absence of sun, clouds, or rain, and then next you will bring in continuous temperature measurements. Using additional regressors can allow you to include more...

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Forecasting Time Series Data with Facebook Prophet
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