MeraTSF: Event-Aware Residual Modeling for Sparse Exogenous Time Series Forecasting

Incorporating exogenous variables into time series forecasting remains challenging, as real-world exogenous information is often sparse and temporally heterogeneous in its effects. Unlike conventional covariates, sparse exogenous events occur infrequently and abruptly, inducing transient disturbances, horizon-dependent responses, and long-term distribution shifts. These effects can be viewed as scale-dependent deviations from the underlying normal temporal dynamics, which are difficult to capture through direct correlation-based modeling. To address this challenge, we propose MeraTSF, a multi-scale residual refinement framework that models exogenous effects as structured residual deviations from normal temporal patterns. MeraTSF decomposes temporal dynamics across multiple scales and uses exogenous information to adaptively refine the corresponding residual representations, enabling the model to capture heterogeneous temporal effects induced by sparse exogenous events. Extensive experiments on multiple real-world datasets show that MeraTSF consistently outperforms state-of-the-art methods across diverse datasets and forecasting settings. Moreover, MeraTSF can be seamlessly integrated into existing forecasting architectures, highlighting its strong plug-and-play capability and broad applicability.