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

Understanding the logistic function

The logistic function generates an S-shaped curve; the equation takes the following form:

Figure 6.2 – The logistic function

Here, L is the maximum value of the curve, k is the logistic growth rate, or steepness, of the curve, and x0 is the x-value of the curve's midpoint.

Taking , , and , the logistic function produces the standard logistic function, seen in the following plot:

Figure 6.3 – The standard logistic function, y = 1 / (1 + e-x)

If you have studied logistic regression or neural networks, you may recognize this as the sigmoid function. Any input value for x, from -∞ to ∞, will be squished into an output value, y, between 0 and 1. This equation is what allows a logistic regression model to accept any input value and output a probability between 0 and 1.

The equation was developed by Pierre François Verhulst, a Belgian mathematician, in a series...