When to Apply Forecast Corrections: Opportunities, Recoverability, and Risk under Delayed Feedback

Lightweight forecast corrections can improve a frozen forecaster on average, but heterogeneous effects make the correct action forecast-dependent, and the decisive loss difference is unavailable when a multi-step forecast is issued. We therefore study correction selection separately from correction generation through a matched delayed-feedback evaluation that freezes each candidate pair and distinguishes average correction capacity, retrospective opportunity, issue-time recovery, exposure, and local downside. In the primary Prompt-Z/PatchTST study, retrospective opportunity appears throughout the evaluated matrix, whereas the tested issue-time policies recover only a limited and heterogeneous share. The learned router improves over simple non-anticipating rules and the minimal linear router, but larger routers, regression-risk-aligned objectives, and simple mixtures do not yield consistent further gains under matched Validation/CAL control. Operatingpoint changes jointly alter recovery and intervention exposure, and positive aggregate recovery can coexist with adverse cells and local periods. A separately audited iTransformer scope check supports the same qualitative separation, while the conclusions remain bounded to the evaluated candidate pairs and open-loop replay