Forecasting graph-based time-dependent data has many practical applications. This task is challenging as models need not only to capture spatial dependency and temporal dependency within the data, but also to leverage useful auxiliary information for accurate predictions. In this paper, we analyze limitations of state-of-the-art models on dealing with temporal dependency. To address this limitation, we propose GSA-Forecaster, a new deep learning model for forecasting graph-based time-dependent data. GSA-Forecaster leverages graph sequence attention (GSA), a new attention mechanism proposed in this paper, for effectively capturing temporal dependency. GSA-Forecaster embeds the graph structure of the data into its architecture to address spatial dependency. GSA-Forecaster also accounts for auxiliary information to further improve predictions. We evaluate GSA-Forecaster with large-scale real-world graph-based time-dependent data and demonstrate its effectiveness over state-of-the-art models with 6.7% RMSE and 5.8% MAPE reduction.
翻译:预测基于图表的时间依赖数据有许多实际应用。 这项任务具有挑战性,因为模型不仅需要在数据中捕捉空间依赖性和时间依赖性,而且还需要利用有用的辅助信息进行准确预测。 在本文中,我们分析了处理时间依赖性的最新模型的局限性。为解决这一局限性,我们提议GSA-Forecaster,这是一个用于预测基于图表的时间依赖数据的新的深层学习模型。 GSA-Forecaster 杠杆序列关注(GSA),这是本文中提议的一种新的关注机制,可有效捕捉时间依赖性。 GSA-Forecaster将数据的图表结构嵌入其结构,以解决空间依赖性问题。 GSA-Forecaster 还为辅助信息进行核算,以进一步改进预测。我们用大规模基于实时图表的时间依赖性数据来评估GSA-Forester,并展示其相对于以6.7%的 RMSE和5.8%的MAPE为削减率的状态艺术模型的有效性。