Text-Guided Multimodal Attentive Graph Learning for Time Series Forecasting with Exogenous Variables

Time series forecasting with exogenous variables predicts endogenous targets using historical observations and associated exogenous information. Existing studies mainly rely on numerical time series, leaving textual semantics largely unexplored. Furthermore, to improve efficiency, many methods simplify inter-variable modeling and fail to capture its full capacity, particularly exogenous-endogenous relationships. To address these limitations, we propose MAGNet, a text-guided Multimodal Attentive Graph Network that integrates textual semantics into graph learning for time series forecasting with exogenous variables. MAGNet first employs an offline pretrained language model to generate contextual descriptions for exogenous and endogenous variables separately, providing global semantic representations beyond numerical observations. The resulting representations are incorporated into an asymmetric heterogeneous graph to better capture hierarchical inter-variable relationships. Building on this graph, predictive causality-aware attention mechanisms learn target-focused graph representations, improving forecasting performance. Experiments on 12 real-world datasets show that MAGNet achieves competitive short- and long-term forecasting performance compared to existing baselines, with ablation and case studies further supporting the effectiveness of the proposed framework.