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

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 …

Multivariate spatiotemporal data arise frequently in practical applications, often involving
complex dependencies across cross-sectional units, time points and multivariate variables …

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 …

Multi-dimensional time series data, such as matrix and tensor-variate time series, are
increasingly prevalent in fields such as economics, finance, and climate science. Traditional …

This paper focuses on the analysis of sequential image data, particularly brain imaging data
such as MRI, fMRI, CT, with the motivation of understanding the brain aging process and …

The objective of this thesis is to model matrix-valued time series in a high-dimensional
framework. To this end, the entire study is presented in a non-asymptotic framework. We first …

In this paper, we study a new two-way factor model for high-dimensional matrix-variate time
series. To estimate the number of factors in this two-way factor model, we decompose the …

The main motivation of this manuscript is to deepen our understanding of phenomena with a
temporal component. Most machine learning algorithms and high-dimensional statistical …

Statistical analysis of high-dimensional functional data/times series arises in various
applications. Examples include different types of brain imaging data in neuroscience (Zhu et …