Vector autoregressive (VAR) models aim to capture linear temporal interdependencies
among multiple time series. They have been widely used in macroeconomics and financial …

There has been considerable advance in understanding the properties of sparse
regularization procedures in high‐dimensional models. In time series context, it is mostly …

Solar forecasting using data collected by satellites or sensor networks is often framed as a
many-predictor spatio-temporal regression problem. Whereas the regressand is the …

This paper studies methods for testing and estimating change-points in the covariance
structure of a high-dimensional linear time series. The assumed framework allows for a large …

Facing increasing domestic energy consumption from population growth and
industrialization, Saudi Arabia is aiming to reduce its reliance on fossil fuels and to broaden …

Detecting an anomaly in multichannel signal data is a challenging task in various domains. It
should take into account the cross-channel relationship and temporal relationship within …

The problem of testing for the equality of autocovariances of two independent high-
dimensional time series is studied. Tests based on the suprema or sums of suitable …

High-dimensional time series datasets are becoming increasingly common in many areas of
biological and social sciences. Some important applications include gene regulatory …

Motivated by the need to analyze readily available data collected in space and time,
especially in environmental sciences, we propose a parsimonious spatiotemporal model for …

Many models for spatial and spatio-temporal data assume that" near things are more related
than distant things," which is known as the first law of geography. While geography may be …