Calibrate First, Remember the Residual: Outcome-Aligned Memory for Forecasting

Online forecasting provides a stream of delayed targets that can be used to correct later predictions. A short residual memory, however, may spend much of its capacity tracking persistent changes in forecast level and scale. We introduce Calibrated-Residual Outcome-Aligned Ridge Memory (CR-OARM), which first applies causal channel-wise affine calibration and then fits a 32-episode ridge memory to the remaining error. The memory groups overlapping forecasts by their target timestamp so that repeated predictions of one outcome do not act as independent evidence. A frozen sample-wise gate controls correction strength. Across six datasets, four horizons, two backbones, and three source seeds, CR-OARM obtains the lowest mean MSE in a 144-case matched comparison (0.3826, versus 0.3865 for calibration, 0.3874 for COSA, and 0.3889 for TAFAS), although paired intervals against the two adapters include zero. In a convergence-controlled replay, a gate retrained on purged validation streams matches calibration (0.3859 versus 0.3858), while the fixed original gate reaches 0.3820 MSE and is reported as a control. A prediction-equivalent compact implementation runs 1.84–2.79× faster than released TAFAS on four GPU cases.