The vector autoregressive (VAR) model has been widely used for modeling temporal
dependence in a multivariate time series. For large (and even moderate) dimensions, the …

This paper focuses on the joint estimation of parameters and topologies of multivariate
graphical autoregressive moving-average (ARMA) processes. Since the graphical structure …

An algorithm is presented for topology selection in graphical models of autoregressive
Gaussian time series. The graph topology of the model represents the sparsity pattern of the …

This paper focuses on the identification of graphical autoregressive models with dynamical
latent variables. The dynamical structure of latent variables is described by a matrix …

The paper proposes an identification procedure for autoregressive Gaussian stationary
stochastic processes under the assumption that the manifest (or observed) variables are …

We propose a novel graphical model selection scheme for high-dimensional stationary time
series or discrete time processes. The method is based on a natural generalization of the …

We consider the problem of inferring the conditional independence graph (CIG) of a sparse,
high-dimensional stationary multivariate Gaussian time series. A sparse-group lasso-based …

We consider linear models for stochastic dynamics. Any such model can be associated a
network (namely a directed graph) describing which degrees of freedom interact under the …

A two-stage sparse vector autoregression method is proposed. It relies on the more recent
and powerful technique of time series graphical lasso to estimate sparse inverse spectral …

In this letter we propose an identification procedure of a sparse graphical model associated
to a Gaussian stationary stochastic process. The identification paradigm exploits the …