Causal Mask in Transformer via Transfer Entropy Estimation from Vector Autoregressive Learning for Multivariate Time Series Forecasting

Time series forecasting remains challenging in domains such as finance and climate science, where complex interactions among variables often induce spurious correlations. We propose ARCausal, a forecasting framework that integrates transfer entropy (TE)-based causal discovery with Transformer attention modeling. ARCausal introduces a sparse causal masking mechanism derived from TE and refined via vector autoregression (VAR) estimation to capture dynamic causal interactions. The mask suppresses noninformative dependencies and distinguishes autocorrelation from cross-variable causal effects, improving both predictive performance and interpretability. Experiments on nine benchmark datasets demonstrate consistent improvements over strong baselines, achieving up to [Formula: see text] reduction in MSE while maintaining computational efficiency. Visualization results further illustrate the interpretability of the learned causal structures. The code is publicly available at https://github.com/jancely/ARCausal/.