Extending Granger causality to nonlinear systems release_23qulijmmvhuvnovjlbo4hwdx4

by Nicola Ancona, Daniele Marinazzo, Sebastiano Stramaglia

Released as a report .

2004  

Abstract

We consider extension of Granger causality to nonlinear bivariate time series. In this frame, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. Not all the nonlinear prediction schemes are suitable to evaluate causality, indeed not all of them allow to quantify how much the knowledge of the other time series counts to improve prediction error. We present a novel approach with bivariate time series modelled by a generalization of radial basis functions and show its application to a pair of unidirectionally coupled chaotic maps and to a physiological example.
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Type  report
Stage   submitted
Date   2004-05-03
Version   v1
Language   en ?
Number  BARI-TH 482/04
arXiv  physics/0405009v1
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