  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.
