Riding The Bayesian Wave: Time Series Forecasting With Bayesian Regression

From Theory to Application: Bayesian Ridge Regression in Action

Bayesian ridge regression stands as a powerful and versatile technique. It is a Bayesian approach to linear regression that offers a unique perspective on modeling and prediction. This approach combines the best of both worlds: the regularization properties of ridge regression and the probabilistic framework of Bayesian inference.

This article presents the basics of this approach and provides a sample code to perform a forecasting task on the S&P 500 index.

Bayesian Ridge Regression

Before delving into Bayesian ridge regression, let’s first understand the foundation upon which it builds. Ridge regression is a linear regression technique designed to tackle the issue of multicollinearity in predictive modeling. When predictors (features) in a dataset are highly correlated, traditional linear regression models can become unstable and yield unreliable coefficient estimates.

Ridge regression addresses this by introducing a regularization term to the linear regression objective function. This regularization term, often referred to as the L2 penalty, encourages the model to distribute the weights…