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

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

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 (V...

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Bibliographic Details
Main Authors: Yuri Antonacci, Ludovico Minati, Luca Faes, Riccardo Pernice, Giandomenico Nollo, Jlenia Toppi, Antonio Pietrabissa, Laura Astolfi
Format: Article
Language:English
Published: PeerJ Inc. 2021-05-01
Series:PeerJ Computer Science
Subjects:
Granger causality
State-space models
Vector autoregressive model
Artificial neural networks
Stochastic gradient descent L1
Multivariate time series analysis
Online Access:https://peerj.com/articles/cs-429.pdf

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https://peerj.com/articles/cs-429.pdf

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