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

Interpreting the forecast DataFrame

Now, let's take a look at that forecast DataFrame by displaying the first three rows (I've transposed it here, in order to better see the column names on the page) and learn how these values were used in the preceding chart:

forecast.head(3).T

After running that command, you should see the following table print out:

Figure 2.4 – The forecast DataFrame

The following is a description of each of the columns in the forecast DataFrame:

  • 'ds': Datestamp or timestamp that values in that row pertain to
  • 'trend': Value of the trend component alone
  • 'yhat_lower': Lower bound of the uncertainty interval around the final prediction
  • 'yhat_upper': Upper bound of the uncertainty interval around the final prediction
  • 'trend_lower': Lower bound of the uncertainty interval around the trend component
  • 'trend_upper': Upper bound of the...