XGBoost with time-aware validation

Advanced forecasting should be judged by horizon, segment and decision value—not one aggregate score. This note examines xgboost with time-aware validation with that evaluation discipline.

Practitioner lens

Forecast distributions from xgboost with time-aware validation become actionable when passed into inventory, sourcing or capacity models. Preserving uncertainty is often more useful than collapsing it too early.

First experiment

Use rolling-origin backtests, horizon-specific losses and interval coverage; keep feature availability time-correct.

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