The deployment of smart grids and renewable energy dispatch centers motivates the
development of forecasting techniques that take advantage of near real‐time measurements …

Causality graphs are routinely estimated in social sciences, natural sciences, and
engineering due to their capacity to efficiently represent the spatiotemporal structure of multi …

The autoregressive (AR) model is widely used in electroencephalogram (EEG) analyses
such as waveform fitting, spectrum estimation, and system identification. In real applications …

Objective: Cortico-muscular communication patterns are instrumental in understanding
movement control. Estimating significant causal relationships between motor cortex …

Multivariate autoregressive modeling is widely considered in neuroscience, especially when
effective connectivity is concerned. In high-dimensional space, the conventional least …

Graphical time series models encode the conditional independence among the variables of
a multivariate time series. An iterative method is proposed to estimate a graphical time …

Granger graphical models explain Granger causality between variables in time series
through an estimation of zero pattern of coefficients in multivariate autoregressive (AR) …

Understanding the interactions between the central nervous system and muscular
responses is essential for developing effective strategies to diagnose and manage …

Identifying the topology that underlies a set of time series is useful for tasks such as
prediction, denoising, and data completion. Vector autoregressive (VAR) model based …

The problems of selecting partial correlation and causality graphs for count data are
considered. A parameter driven generalized linear model is used to describe the observed …