… In this article, we propose a versatile sparsity-controlled VAR model which enables a proper
visualization of potential causalities while allows the user to control different dimensions of …

… sparsity-controlled vector autoregressive (SC-VAR) model is introduced to obtain sparse
model … framework by reformulating the original VAR model into a constrained mixed integer …

… and SCAD, we propose a novel method for learning VAR model structures. We refer to this
method as pseudo-likelihood vector autoregression (PLVAR) because it utilizes a pseudo-…

… the general function (see below for details) to sum up the values of all the regularized
parameters by a user-defined penalty method, with the level of regularization or sparsity controlled …

… of the p th order vector autoregressive VAR p model under the multivariate skew- t -normal
(MSTN) innovations. The VAR p time series model has been applied in describing several …

… efficient vector autoregressive moving average (VARMA) based intraday WPF model that …
The correlation-constrained sparsity-controlled VAR model has outstanding performance for …

… However, most of them suffer from huge ID search space and signature collision during CS
recovery process. To address these issues, we propose SCRIC, a novel sparsity controlled …

… , eg integrating Vector Auto-Regressive and Neural Network-based models, were proposed
… and sparsity controlled VAR to reduce the effective number of parameters in model training. …

… (1) processes with non-Gaussian noise, the time-reversal remnants distribution of the linear
vector AR(1) model is assumed closer to the Gaussian distribution than the actual remnants …