Ensemble forecasting is the practice of running many distinct forecasting models on the same commodity and combining their outputs into a single probability-weighted forecast. The premise: any single model has its own structural biases (a seasonal model under-reacts to regime shifts, a tree-based model over-reacts to outliers, a deep-learning model overfits sparse history) — but a properly weighted ensemble across many models cancels much of that bias and exposes the uncertainty that remains.
A well-built ensemble produces two things a single model cannot. First, a central forecast that is more robust across regimes — the price level the curve is pointing at is no longer a single model’s guess. Second, an explicit probability distribution around that central forecast — bull, base, bear scenarios with quantified likelihoods. That distribution is what a procurement team actually needs to hedge against, because they’re not committing to the mean, they’re committing under uncertainty.
INAYA’s Market Foresight is built on this approach: a portfolio of models per commodity, weighted dynamically based on recent regime fit, producing probability-weighted bull/base/bear scenarios over a 1–24 month horizon. Live in production for over 5 years, with directional accuracy exceeding 90% on the 1–6 month horizon for liquid contracts.
Related concepts: directional accuracy (how forecast quality is measured), commodity intelligence (what the forecast becomes once exposure is mapped).