Glossary · E-commerce ML
Conformal Prediction
Definition
Conformal prediction is a model-agnostic framework for producing calibrated prediction intervals with finite-sample coverage guarantees. Applied to demand forecasting it replaces opaque point predictions with intervals that provably contain the true demand at a specified confidence level.
Rather than relying on the parametric assumptions of classical prediction intervals, conformal prediction uses a held-out calibration set to produce a non-conformity score distribution and intervals with guaranteed marginal coverage. Split conformal, cross-conformal, and adaptive conformal inference are variants. Particularly valuable for demand forecasting where downstream decisions need honest uncertainty, not false precision.
Essays on this concept
- E-commerce ML
Demand Forecasting with Conformal Prediction: Reliable Uncertainty Intervals for Inventory Optimization
Your demand forecast says you'll sell 1,000 units next month. How confident is that prediction? Traditional models give you a number without honest uncertainty bounds. Conformal prediction gives you intervals with mathematical coverage guarantees — no distributional assumptions required.
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