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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 10: Uncertainty Intervals

Forecasting is essentially predicting the future, and with any prediction, there will necessarily be a particular amount of uncertainty. Quantifying this uncertainty provides the analyst with an understanding of how reliable their forecasts are and it provides the manager with the confidence to stake a lot of capital on a decision.

Prophet was designed from the ground up with uncertainty modeling in mind. Although you interact with it in either Python or R, the underlying model is built in the Stan programming language, a probabilistic language that allows Prophet to perform Bayesian sampling in an efficient manner to provide a deeper understanding of the uncertainty in the model, and thus the business risk of the forecast.

There are three sources of uncertainty that contribute to the total uncertainty in your Prophet model:

  • Uncertainty in the trend
  • Uncertainty in the seasonality, holidays, and additional regressors
  • Uncertainty...
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Forecasting Time Series Data with Facebook Prophet
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