Graph neural forecasting across networks

A technically strong model can still mislead if validation leaks future information. This brief explores graph neural forecasting across networks and the checks needed for credible comparison.

Practitioner lens

Model graph neural forecasting across networks at the granularity the business needs, but inspect whether reconciliation or aggregation changes bias, variance or the shape of the predictive distribution.

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.