ExoST: Multi-System Exogenous Variable Modeling for Spatio-Temporal Forecasting

Spatio-temporal (ST) forecasting is crucial for modeling physical world systems. However, existing ST models primarily focus on target variables within a single system, often neglecting their interactions with external systems. Although early research attempts introduced exogenous variables, they merely treated the spatial dimension as an external auxiliary feature, essentially belonging to covariate modeling within a single system. This paper first conducts a systematic study on the modeling of multi-system exogenous variables in ST forecasting. We identify two core challenges in this field: the inconsistent effects of distinct system on the target variables and the imbalance effects between historical and future information. To tackle this, we propose ExoST, a simple yet effective exogenous variable modeling general framework highly compatible with existing ST backbones that follows a "select-then-balance" paradigm. ExoST can adaptively select informative signals from multi-system exogenous variables and perform context-aware balance of historical and future exogenous information. Extensive experiments on real-world datasets demonstrate that ExoST consistently enhances the predictive performance of various backbones while maintaining its simplicity, robustness, and efficiency.