Extending Granger causality to nonlinear systems

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dc.creatorAncona, Nicola
dc.creatorMarinazzo, Daniele
dc.creatorStramaglia, Sebastiano
dc.date2004-05-03
dc.date.accessioned2026-07-07T05:51:48Z
dc.date.available2026-07-07T05:51:48Z
dc.descriptionWe 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.
dc.description8 pages, 4 figures
dc.identifierhttps://arxiv.org/abs/physics/0405009
dc.identifierhttp://arxiv.org/abs/physics/0405009
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/86113
dc.subjectData Analysis, Statistics and Probability
dc.subjectMedical Physics
dc.subjectQuantitative Methods
dc.titleExtending Granger causality to nonlinear systems
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