Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in
many real-world applications such as monitoring urban traffic and air quality. Making …

This paper briefly reviews the recent research in matrix‐variate time series analysis,
discusses some new developments, especially for seasonal time series, and demonstrates …

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 …

This article considers the estimation and inference of the low-rank components in high-
dimensional matrix-variate factor models, where each dimension of the matrix-variates (p× …

Forecasting short-term ridership of different origin-destination pairs (ie, OD matrix) is crucial
to the real-time operation of a metro system. However, this problem is notoriously difficult …

We consider to model matrix time series based on a tensor canonical polyadic (CP)-
decomposition. Instead of using an iterative algorithm which is the standard practice for …

Factor model is an appealing and effective analytic tool for high-dimensional time series,
with a wide range of applications in economics, finance and statistics. This paper develops …

We study stochastic optimization algorithms for constrained nonconvex stochastic
optimization problems with Markovian data. In particular, we focus on the case when the …

Many data sets in biology, medicine, and other biostatistical areas deal with matrix‐valued
time series. The case of a single univariate time series is very well developed in the …

This paper investigates the issue of determining the dimensions of row and column factor
spaces in matrix-valued data. Exploiting the eigen-gap in the spectrum of sample second …