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Factor Models for High-Dimensional Tensor Time Series
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AI
DOI：10.1080/01621459.2021.1912757.png)
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摘要
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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.
Keyword:
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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