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

Detecting outliers automatically

In these examples so far, we detected outliers with a simple visual inspection of the data and applied common sense. In a fully automated setting, defining logical rules for what we as humans do intuitively can be difficult. Outlier detection is a good use of an analyst's time as we humans are able to use much more intuition, domain knowledge, and experience than a computer can. But as Prophet was developed to reduce the workload of analysts and automate as much as possible, we'll examine a couple of techniques to identify outliers automatically.

Winsorizing

The first technique is called Winsorization, named after the statistician Charles P. Winsor. It is also sometimes called clipping. Winsorization is a blunt tool and tends not to work well with non-flat trends. Winsorization requires the analyst to specify a percentile; all data above or below that percentile is forced to the value at the percentile.

Trimming is a similar technique...