Forecast Revisions Should Be News: Instrumented Martingale Regularization for Multi-Horizon Forecasting

Forecasts are usually scored one issue date at a time, yet deployed systems repeatedly revise the same target. If yesterday's information predicts today's revision, the earlier forecast left usable signal on the table; revisions caused by genuinely new information are desirable, so penalizing their magnitude solves a different problem. We formalize revision efficiency through the martingale restriction of conditional-mean forecasts and turn it into an instrumented objective for direct multi-horizon models. A naive squared minibatch moment contains a positive diagonal term proportional to revision variance, silently rewarding stability. Moment-U removes this self-product with an off-diagonal U-statistic. We prove population compatibility, exact conditional unbiasedness for shuffled finite-series batches, and an O(1/n) bias bound under summable temporal covariance. In a heteroskedastic-news control, Moment-U preserves legitimate news while diagonal and stability losses shrink it. A fixed validation rule selects Moment-U on five of six DLinear datasets with negligible MSE changes. Across five-seed PatchTST/iTransformer studies, a frozen richer kernel score falls 6.6–40.3% in all four backbone–dataset cells and is below validation-budget-matched stability in each; MSE shows one small cost, two unresolved changes, and one gain. Including DLinear, a frozen falsification gate passes in three of four headline cells. We reduce predictable revisions rather than claim complete conditional calibration.