Factor Models for High-Dimensional Tensor Time Series
Methodology
2020-05-20 v2 Statistics Theory
Statistics Theory
Abstract
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.
Cite
@article{arxiv.1905.07530,
title = {Factor Models for High-Dimensional Tensor Time Series},
author = {Rong Chen and Dan Yang and Cun-hui Zhang},
journal= {arXiv preprint arXiv:1905.07530},
year = {2020}
}