Prediction intervals and coverage

Advanced methods need monitoring as well as training. This short read considers prediction intervals and coverage and how to detect when yesterday’s fit no longer holds.

Practitioner lens

Check how prediction intervals and coverage behaves under regime changes and sparse data. A method should be monitored for degradation and have a practical fallback when assumptions stop holding.

First experiment

Monitor feature drift, residual structure and changing error by segment after deployment.

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