Let’s Use Ridge Regression to Predict Time Series
Using Python to Predict Time Series With Ridge Regression
Ridge regression is a regression technique that adds a penalty term to the traditional linear regression model, aiming to prevent overfitting by shrinking the coefficients of the predictors towards zero. This regularization helps to handle multicollinearity and stabilize the estimates, making the model more robust, especially when dealing with datasets with high dimensionality or highly correlated predictors.
Traditional linear regression techniques may struggle if the predictors are highly correlated or if there are many predictors relative to the sample size. Ridge regression, with its ability to handle multicollinearity and stabilize coefficient estimates, may offer a solution by providing more reliable predictions and better model performance on stationary time series data.
This article presents a simple way to create a Ridge regression model in Python to predict the returns of the S&P 500 index.
Introduction to Ridge Regression
Linear regression is a common method used to predict the relationship between independent variables (features) and a dependent variable (outcome). It works by fitting a straight line to the data points. However, in…
