Co-evolving time series appears in a multitude of applications such as
environmental monitoring, financial analysis, and smart transportation. This
paper aims to address the following challenges, including (C1) how to
incorporate explicit relationship networks of the time series; (C2) how to
model the implicit relationship of the temporal dynamics. We propose a novel
model called Network of Tensor Time Series, which is comprised of two modules,
including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural
Network (TRNN). TGCN tackles the first challenge by generalizing Graph
Convolutional Network (GCN) for flat graphs to tensor graphs, which captures
the synergy between multiple graphs associated with the tensors. TRNN leverages
tensor decomposition to model the implicit relationships among co-evolving time
series. The experimental results on five real-world datasets demonstrate the
efficacy of the proposed method.

本篇论文提出了一种名为 Tensor Time Series Network 的新模型，它包括两个模块，解决了如何结合时间序列的关系网络和如何建模时间动态的隐含关系等问题，并在五个真实数据集上的实验结果证明了该方法的有效性。