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

Correcting outliers that cause wide uncertainty intervals

In the first type of outlier we looked at, the problem was that the seasonality was affected and forever changed yhat in the forecast (if you remember from Chapter 2, Getting Started with Facebook Prophet, yhat is the predicted value for future dates contained in Prophet's forecast DataFrame). In this second problem, yhat is minimally affected but the uncertainty intervals widen dramatically.

To simulate this issue, we need to modify our NatGeo data a bit. Let's say that Instagram introduced a bug in their code that capped likes to 100,000 per post. It somehow went unnoticed for a year before being fixed, but unfortunately, all likes above 100,000 were lost. Such an error would look like this:

Figure 9.6 – Capped likes on National Geographic's Instagram account

You can simulate this new dataset yourself with the following code:

df3 = df.copy()
df3.loc[df3['ds'].dt...