Factor Models for High-Dimensional Tensor Time Series

Research output: Contribution to journalArticlepeer-review

Abstract

Large tensor (multi-dimensional array) data routinely appear nowadays 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 article we present a factor model approach to the analysis of high-dimensional dynamic tensor time series and multi-category dynamic transport networks. This article presents two estimation procedures along with their theoretical properties and simulation results. We present two applications to illustrate the model and its interpretations.

Original languageEnglish (US)
JournalJournal of the American Statistical Association
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Autocovariance matrices
  • Cross-covariance matrices
  • Dimension reduction
  • Dynamic transport network
  • Eigen-analysis
  • Factor models
  • Import–export
  • Tensor time series
  • Traffic
  • Unfolding

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