algo-forecast-ensemblelisted

# 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 ``` IRON LAW: Simple Average Often Beats Complex Combination The "forecast combination puzzle" (Stock & Watson, 2004): equal-weight averaging of diverse models frequently outperforms sophisticated weighting schemes. This is because weight estimation introduces noise that offsets the theoretical gain. Start with simple average and only move to weighted combination if you have abundant validation data. ``` ### Phase 1: Input Validation Generate forecasts from 3+ diverse models (e.g., ARIMA, ETS, Prophet, ML-based). Ensure models are truly diverse (different assumptions/approaches). **Gate:** 3+ model forecasts available, models use different methodologies. ### Phase 2: Core Algorithm **Simple average:** ŷ_ensemble =