SCEU: STEP-CONDITIONED EXOGENOUS UTILITY LEARNING FOR TIME SERIES FORECASTING

Exogenous variables provide valuable forecasting context, but their usefulness can vary across variables and future prediction steps. Existing methods organize cross-variable information through channel-independent processing, channel-dependent interaction, or selective clustering, but these interaction strategies alone do not specify how exogenous information should be weighted at each future prediction step. To address this limitation, we propose SCEU, a Step-Conditioned Exogenous Utility learning framework. SCEU constructs an endogenous-only base forecast, assigns relative Channel Utility scores to exogenous variable-scale representations, and derives Temporal Utility representations from multi-scale endogenous historical patches. A gated bounded residual pathway selectively applies the correction obtained by fusing the two branches while controlling its magnitude at each forecast step. The learned utility weights serve as model-internal, step-conditioned scores for weighting endogenous and exogenous representations. Across diverse benchmarks, SCEU achieves strong forecasting performance. Ablation studies show that Channel Utility, Temporal Utility, and the adaptive response range generally improve forecasting accuracy in the evaluated settings. Overall, SCEU provides a step-conditioned mechanism for selectively utilizing exogenous information in time series forecasting. The code is available at: https://anonymous.4open.science/r/sceu-EBCE.