Breaking Down Forecasting Ensembles: Bias, Variance, and Covariance

One of the most powerful ideas in predictive modeling is the use of ensembles: instead of relying on a single forecast, we average across multiple models. Ensembles almost always outperform individual models—but why?

The answer lies in the bias-variance-covariance decomposition of the expected mean squared error (MSE). This framework gives us a mathematical way to understand how ensembles reduce error and under what conditions they shine