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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 9: Outliers and Special Events

An outlier is any data point that lies significantly away from the other data points along one or multiple different axes. Outliers may be incorrect data, such as resulting from a miscalibrated sensor producing invalid data, or even a finger slip on the keyboard during data entry, or they may be accurately recorded data that happens to wildly miss historical trends for various reasons, such as if a tornado passed over a wind speed sensor.

These uncharacteristic measurements will sway any statistical or machine learning model, and so correcting outliers is a challenge throughout data science and statistics. Fortunately, Prophet is generally robust to mild outliers. With extreme outliers though, there are two problems Prophet can experience: one problem with seasonality and another with uncertainty intervals.

In this chapter, you'll see examples of both of these problems and learn how to alleviate their effects on your forecast. You...

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Forecasting Time Series Data with Facebook Prophet
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