SAR-LCRT: RISK-GATED RESIDUAL TRANSPORT FOR FROZEN TIME-SERIES FORECASTERS UNDER DELAYED FEEDBACK

Accurate time-series forecasts support planning in energy, transportation, and environmental systems. Yet a trained model can repeatedly overestimate or underestimate future values, and retraining it after deployment may be costly. Correcting these errors presents two challenges: error patterns vary with the input, and the usefulness of a correction can be assessed only after the predicted time interval has elapsed. We propose SAR-LCRT, a framework that improves a trained forecaster without changing its parameters. Low-Rank Conditional Residual Transport (LCRT) learns patterns in prediction errors across future steps and variables, using validation data to construct input-dependent corrections. Structure-Adaptive Risk Control (SAR) then chooses whether to use a correction or retain the original forecast, based on observed outcomes of past forecasts. The framework compares all available corrections to guide selection. It separately tracks the gains and losses of the forecasts actually used and stops correction when the accumulated evidence no longer supports it. Across eight architectures and 13 datasets, SAR-LCRT matches or improves the original models’ MSE on 86.1% of 416 forecasting tasks, based on three-seed means. On PEMS08, it reduces MSE by 11.06% on average across architectures and forecast horizons. These results demonstrate the value of combining structured error correction with delayed-feedback selection as a reusable addition to trained forecasters.