Regret metrics for forecast evaluation

Model complexity should earn its place through better decisions, not novelty. This article explores regret metrics for forecast evaluation and the baselines needed to establish its incremental value.

Practitioner lens

For regret metrics for forecast evaluation, measure solution feasibility and runtime alongside the objective. A theoretically stronger model may be less valuable if planners cannot obtain a usable answer on time.

First experiment

Document assumptions and version the data, code, parameters and scenario set for reproducibility.

This is an original educational brief. It is a starting point for discussion, not a claim that one technique fits every supply chain.