Neuromuscular disorders (such as stroke and spinal cord injuries) can lead to nerve
damage that profoundly affects a patient's ability to control limb movements. Analyzing and …

The standard vector autoregressive (VAR) models suffer from overparameterization which is
a serious issue for high-dimensional time series data as it restricts the number of variables …

Commercial aviation is a major contributor to the US economy, directly or indirectly
generating approximately US $680 billion, or 4% of GDP, and supporting millions of jobs …

Time-series data are widespread and have inspired numerous research works in machine
learning and data analysis fields for the classification and clustering of temporal data. While …

Vector autoregressive (VAR) models have become a popular choice for modeling
multivariate time series data due to their simplicity and ease of use. Efficient estimation of …

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 …

Due to the increasing development of information technologies and their applications in
many scientific fields, high-dimensional time series data are routinely collected across a …

Stacked generalization is a commonly used technique for improving predictive accuracy by
combining less expressive models using a high-level model. This paper introduces a …

Data observed simultaneously in both space and time are becoming increasingly prevalent
with applications in diverse areas, from ecology to financial econometrics. The datasets are …