GRAIL: Granger-Inspired Causal Masking Enhanced Multivariate Time Series Forecasting

Multivariate time series forecasting is essential for a wide range of real-world applications, including finance, energy, climate analysis, and transportation. However, accurate and interpretable forecasting remains challenging due to heterogeneous temporal dynamics and complex cross-variable dependencies. Existing approaches often rely on fixed temporal modeling strategies or dense channel interactions, making it difficult to capture sparse, directional, and delayed predictive relationships among variables. In this work, we propose GRAIL, a framework that jointly models adaptive temporal patterns and lag-aware cross-channel dependencies for multivariate forecasting. The proposed method introduces a Temporal Scale Fusion module to integrate short-, base-, and long-range temporal representations through sample-level adaptive weighting. It further incorporates a Granger-inspired dependency modeling module that estimates multi-step lagged predictive relationships and constructs sparse masks to guide cross-variable information aggregation. By combining adaptive temporal representation learning with structure-aware dependency modeling, the proposed framework improves forecasting accuracy while providing enhanced interpretability. Extensive experiments on eight real-world benchmark datasets demonstrate that GRAIL achieves state-of-the-art performance.