Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators.

ABSTRACT: One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Square (OLS) estimation, a viable alternative is to use Artificial Neural Networks (ANNs) implemented i

SUBMITTER: Antonacci Y 

PROVIDER: S-EPMC8157130 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature