When Should a Forecast Be Corrected? Prediction Quality Is Not Decision Quality

Post-hoc forecasting systems can leave two predictions for the same future target: a baseline forecast and a proposed revision. The system must decide whether to apply the revision before the target, and hence its true benefit, is known. We study this candidate-conditioned decision as forecast override. Under squared error, a revision's realized benefit depends on its alignment with the baseline residual and its magnitude; forecasts with similar errors can therefore favor opposite actions. We evaluate preserve-or-apply policies with Selection Regret, the excess loss relative to choosing the better action in hindsight. On Weather, a residual-based policy has regret 0.01164, compared with 0.03173 for preserving and 0.09048 for applying every revision. On ETTm2, its point-estimate regret is higher than that of the calibration-selected constant action, although the paired interval includes zero. Across forecasting backbones and candidate sources, predictive scores and average correction gains do not consistently rank execution policies. These results motivate evaluating a proposed revision as a decision whose value depends on the candidate and the information available when it is executed.