Forecasting under covariate shift

The central test for forecasting under covariate shift is whether it generalizes across time and operating conditions. Use rolling evaluation and compare against a simple, defensible baseline.

Practitioner lens

Forecast distributions from forecasting under covariate shift become actionable when passed into inventory, sourcing or capacity models. Preserving uncertainty is often more useful than collapsing it too early.

First experiment

Propagate predictive distributions into the downstream inventory or capacity decision where possible.

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