  Large tensor (multi-dimensional array) data are now routinely collected in a
wide range of applications, due to modern data collection capabilities. Often
such observations are taken over time, forming tensor time series. In this
paper we present a factor model approach for analyzing high-dimensional dynamic
tensor time series and multi-category dynamic transport networks. Two
estimation procedures along with their theoretical properties and simulation
results are presented. Two applications are used to illustrate the model and
its interpretations.
