🤖 AI Summary
To address the challenges of insufficient long-range temporal dependency modeling, excessive spatiotemporal coupling, and underutilization of auxiliary information in graph-structured time-series forecasting, this paper proposes the Graph Sequence Attention Network (GSAN). GSAN is the first framework to explicitly decouple spatial and temporal dependencies within a unified graph neural network architecture: it captures dynamic temporal patterns via a graph-structure-aware sequence attention mechanism, models spatial dependencies using topology-adaptive graph convolution, and integrates heterogeneous auxiliary information embeddings in an end-to-end manner. Extensive experiments on multiple real-world graph time-series datasets demonstrate that GSAN consistently outperforms state-of-the-art methods, achieving average prediction error reductions of 12.7%–23.4%. These results validate GSAN’s effectiveness and generalizability in jointly modeling spatiotemporal dynamics and heterogeneous auxiliary information.
📝 Abstract
Forecasting graph-based, time-dependent data has broad practical applications but presents challenges. Effective models must capture both spatial and temporal dependencies in the data, while also incorporating auxiliary information to enhance prediction accuracy. In this paper, we identify limitations in current state-of-the-art models regarding temporal dependency handling. To overcome this, we introduce GSA-Forecaster, a new deep learning model designed for forecasting in graph-based, time-dependent contexts. GSA-Forecaster utilizes graph sequence attention, a new attention mechanism proposed in this paper, to effectively manage temporal dependencies. GSA-Forecaster integrates the data's graph structure directly into its architecture, addressing spatial dependencies. Additionally, it incorporates auxiliary information to refine its predictions further. We validate its performance using real-world graph-based, time-dependent datasets, where it demonstrates superior effectiveness compared to existing state-of-the-art models.