Ensemble Forecasting

Overview

Ensemble forecasting combines predictions from multiple models to reduce variance and improve accuracy. Simple average of 3-5 diverse models often outperforms the best individual model. Methods: equal-weight average, inverse-error weighting, stacking with a meta-learner. The "forecast combination puzzle" shows simple averaging is hard to beat.

When to Use

Trigger conditions:

• Multiple forecasting models are available and perform similarly
• Reducing forecast risk is more important than maximum accuracy
• Building a production pipeline that's robust to model failure

When NOT to use:

• When one model clearly dominates all others (just use that model)
• When computational budget only allows one model

Algorithm