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Empirical · Machine learning / AI forecasting · Subgroup
Ensembling and combination methods that pool the forecasts from multiple underlying models to reduce variance and hedge model risk.
What this subgroup is for
Reach for forecast combination when no single model is clearly best and you want to average out model risk by pooling several reasonable specifications.
Reference coverage
1 model linked in this subgroup's empirical reference coverage.
Models currently covered
Each model below has its own empirical reference page with overview, graph, proof, and comparison material.
Combination of several base forecasters -- simple averages, weighted blends, or stacking -- to reduce single-model risk.
Best for: When no single model is reliably best and combining several produces a steadier out-of-sample read.
Back to the cluster
The cluster page lists every model in this empirical class, including the ones that do not yet have a fifth-layer subgroup.