Ensemble Forecasting

A single model has a single perspective. An ensemble of models has multiple perspectives. The ensemble prediction — the weighted average of multiple model predictions — is almost always more accurate than any individual model. The reason is variance reduction: each model makes different errors. When the errors are uncorrelated, averaging them out produces a more stable, more accurate prediction. The ensemble for revenue forecasting: combine ARIMA (captures seasonality), regression (captures causal factors), and a gradient-boosted model (captures non-linear patterns). Weight each by its historical accuracy. The ensemble accuracy exceeds any individual model by 5-15% in most business forecasting contexts.