We consider a setting where multiple entities inter-act with each other over time and the time-varying statuses of the entities are represented as multiple correlated time series. For example, speed sensors are deployed in different locations in a road network, where the speed of a specific location across time is captured by the corresponding sensor as a time series, resulting in multiple speed time series from different locations, which are often correlated. To enable accurate forecasting on correlated time series, we proposes graph attention recurrent neural networks.First, we build a graph among different entities by taking into account spatial proximity and employ a multi-head attention mechanism to derive adaptive weight matrices for the graph to capture the correlations among vertices (e.g., speeds at different locations) at different timestamps. Second, we employ recurrent neural networks to take into account temporal dependency while taking into account the adaptive weight matrices learned from the first step to consider the correlations among time series.Experiments on a large real-world speed time series data set suggest that the proposed method is effective and outperforms the state-of-the-art in most settings. This manuscript provides a full version of a workshop paper [1].
翻译:我们考虑一个设置,让多个实体在一段时间内彼此互动,各实体的时间变化状态以多个相关时间序列的形式出现。例如,公路网络中在不同地点部署速度传感器,相应的传感器将一个特定地点的时间速度作为一个时间序列记录在不同的不同地点,导致不同地点的多重速度时间序列,而这些时间序列往往相互关联。为了能够准确预测相关时间序列,我们提议图形注意经常性神经网络。首先,我们考虑到空间相近性,在不同实体之间建立一个图表,并采用多头关注机制,为图表得出适应性加权矩阵,以在不同时间戳中捕捉脊椎(例如不同地点的速度)的相互关系。第二,我们使用经常性神经网络来考虑时间依赖性,同时考虑到从第一步学到的适应性加权矩阵,以考虑时间序列之间的相互关系。大型真实世界速度时间序列数据显示,拟议方法是有效的,超越了大多数环境中的状态。本手稿提供了一份完整的讲习班文件版本。