DMRC: Residual-Manifold Memory Adaptation for Frozen Time-Series Forecasters

Retrieval augmentation methods have achieved notable success in time series forecasting by reusing historical input information. However, when events such as trend drifts or changes in volatility alter the prediction errors associated with similar inputs, these methods may struggle to retrieve historical correction information applicable to the current forecast.To address this limitation, we propose Dynamic Memory-enhanced Residual Correction (DMRC), which adapts frozen forecasters by dynamically adjusting the retrieval of historical residual prototypes according to their relevance to current forecasts. DMRC constructs hierarchical residual prototypes offline and retrieves complementary principal-direction and orthogonal residual components for forecast correction. During online forecasting, multi-view and temporal-overlap consistency checks provide label-free feedback to update retrieval priorities for the principal-direction component, reducing reliance on input similarity alone when historical error patterns become less applicable. This mechanism enables adaptive use of historical correction information while keeping both the forecaster and memory contents frozen. Across seven datasets, four forecast horizons, and six frozen forecasters, DMRC consistently outperforms the frozen baseline, with MSE reductions of up to 15.10%.