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.

翻译：预测基于图的时序数据具有广泛的实际应用，但也面临诸多挑战。有效的模型必须同时捕捉数据中的空间依赖性与时间依赖性，并整合辅助信息以提高预测精度。本文指出了当前最先进模型在处理时间依赖性方面存在的局限。为克服这一问题，我们提出了GSA-Forecaster，一种专为图结构时序场景设计的新型深度学习模型。GSA-Forecaster采用本文提出的新型注意力机制——图序列注意力，以有效处理时间依赖性。该模型将数据的图结构直接整合到其架构中，从而解决空间依赖性问题。此外，它还融合了辅助信息以进一步优化预测结果。我们使用真实世界的图结构时序数据集验证了其性能，结果表明该模型相较于现有最先进模型具有更优的效能。