  Tensor time series (TTS) data, a generalization of one-dimensional time
series on a high-dimensional space, is ubiquitous in real-world scenarios,
especially in monitoring systems involving multi-source spatio-temporal data
(e.g., transportation demands and air pollutants). Compared to modeling time
series or multivariate time series, which has received much attention and
achieved tremendous progress in recent years, tensor time series has been paid
less effort. Properly coping with the tensor time series is a much more
challenging task, due to its high-dimensional and complex inner structure. In
this paper, we develop a novel TTS forecasting framework, which seeks to
individually model each heterogeneity component implied in the time, the
location, and the source variables. We name this framework as GMRL, short for
Gaussian Mixture Representation Learning. Experiment results on two real-world
TTS datasets verify the superiority of our approach compared with the
state-of-the-art baselines. Code and data are published on
https://github.com/beginner-sketch/GMRL.
