NMask: A Lightweight Network via Masked Alignment for Time Series Forecasting with Exogenous Variables

Time series forecasting with exogenous variables is crucial in many real-world applications, as exogenous information often provides complementary signals beyond the target series itself. In particular, future exogenous variables, when available, offer informative priors about future dynamics and can substantially improve forecasting performance. However, existing methods often treat future exogenous variables as auxiliary features to be fused with historical observations, leading to heavy architectures and inefficient interaction modeling. In this paper, we revisit this problem from a new perspective and propose NMask, a lightweight network that explicitly leverages the inherent temporal correspondence between future exogenous variables and future prediction targets. Through a Masked Alignment mechanism, NMask associates learnable tokens with future exogenous information for future endogenous forecasting, while a Target-Conditioned Exogenous Attention module captures delayed effects of exogenous variables by modeling short-term lags and long-term temporal dependencies. Extensive experiments on multiple datasets demonstrate that NMask consistently outperforms existing forecasting methods with exogenous variables, with significantly fewer parameters and lower computational cost. Code is available at https://anonymous.4open.science/r/NMask.