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

Updating a fitted model

Forecasting is unique among predictive models in that the value of the data is its recency and each passing moment creates a new set of valuable data to use. A common situation with a forecast model is the need to refit it as more data comes in. The city of Baltimore, for example, may use the crime model to predict how many crimes they might expect to happen tomorrow, so as to better place their officers in advance. Once tomorrow arrives, they can record the actual data, retrain their model, and predict for the next day.

Prophet is unable to handle online data, which means it cannot add a single new data observation and quickly update the model. Prophet must be trained offline—the new observation will be added to the existing data and the model will be completely retrained. But it doesn't have to be completely retrained from scratch and the following technique will save a lot of time when retraining.

Prophet is essentially an optimization problem...