ExoRAF: Exogenous Residual Retrieval for Time Series Forecasting

Effectively leveraging exogenous variables remains a longstanding challenge in real-world time series forecasting. The key insight of this paper is that exogenous variables are more effectively utilized by modeling forecasting residuals rather than directly predicting the target series. While an endogenous-only backbone captures the intrinsic temporal dynamics of the target series, exogenous variables mainly explain the scenario-dependent residual variations that cannot be captured by historical observations alone. Moreover, similar exogenous contexts often induce similar residual patterns. Motivated by the insight, we propose ExoRAF, an exogenous residual retrieval method for time series forecasting. ExoRAF builds a residual memory from historical exogenous contexts, retrieves relevant residual information using the current exogenous context, and leverages the retrieved residual information to generate correction terms that refine the backbone forecasts. The resulting decoupling of endogenous temporal modeling and exogenous residual explanation makes ExoRAF a plug-and-play module for existing forecasting models. Extensive experiments on diverse real-world datasets demonstrate that ExoRAF consistently improves strong forecasting backbones across a wide range of application scenarios.