RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting
RATL is a plug-in residual-retrieval and feedback-correction method for multivariate time-series forecasting. It freezes a base forecaster to construct retrieval keys and turns historical forecast residuals into a train-only memory. At inference, it retrieves residual trajectories from similar historical contexts under causal availability constraints and uses a set-aware router to select and combine them. Experiments show historical residuals matched to the current context contain reusable forecasting information and RATL improves frozen base forecasters.
A research paper introduces RATL, a method that enhances multivariate time-series forecasting by retrieving and reusing historical forecast residuals. RATL freezes a base forecaster, builds a train-only memory of its residuals, and at inference retrieves similar residual trajectories under causal constraints, then combines them via a set-aware router. Experimental results indicate that matched historical residuals provide reusable information and improve frozen base forecasters.
RATL treats historical residuals as a model-specific memory, enabling non-parametric correction of frozen base forecasters. The set-aware router operates over forecast blocks and variables, suggesting a structured approach to combining retrieved residual trajectories. Causal availability constraints ensure retrieved residuals do not leak future information.
The plug-in nature of RATL implies it can be applied to existing forecasting models without retraining the base model, potentially lowering adoption barriers in time-series applications. The focus on robustness to differences in output level, numerical scale, and local dynamics addresses common deployment challenges in multivariate forecasting.
Improved forecasting accuracy for frozen base models could reduce retraining costs and improve reliability in applications such as demand forecasting, energy load prediction, and financial time-series analysis. The plug-in design may enable incremental upgrades to existing systems.
Observable next signals include follow-up papers applying RATL to other forecasting domains, open-source implementations, or benchmarks comparing RATL against other retrieval-augmented regression methods. Adoption in industrial forecasting pipelines would be a strong validation signal.