Modern data collection capabilities have led to massive quantity of time series. Large tensor (or multi-dimensional array) data are now routinely collected in a wide range of applications, and often such observations are taken over time, forming tensor time series. In this talk we present a factor model approach for analyzing high-dimensional dynamic tensor time series. Specifically we develop a general class of tensor factor models, with modifications for specific applications, in modeling matrix- and tensor-valued time series and dynamic networks. Estimation procedures along with their theoretical properties, numerical results and applications will be presented.