dorsal/arxiv
View SchemaExtending Granger causality to nonlinear systems
| Authors | Nicola Ancona, Daniele Marinazzo, Sebastiano Stramaglia |
|---|---|
| Categories | |
| ArXiv ID | physics/0405009 |
| URL | https://arxiv.org/abs/physics/0405009 |
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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"abstract": "We consider extension of Granger causality to nonlinear bivariate time\nseries. In this frame, if the prediction error of the first time series is\nreduced by including measurements from the second time series, then the second\ntime series is said to have a causal influence on the first one. Not all the\nnonlinear prediction schemes are suitable to evaluate causality, indeed not all\nof them allow to quantify how much the knowledge of the other time series\ncounts to improve prediction error. We present a novel approach with bivariate\ntime series modelled by a generalization of radial basis functions and show its\napplication to a pair of unidirectionally coupled chaotic maps and to a\nphysiological example.",
"arxiv_id": "physics/0405009",
"authors": [
"Nicola Ancona",
"Daniele Marinazzo",
"Sebastiano Stramaglia"
],
"categories": [
"physics.data-an",
"physics.med-ph",
"q-bio.QM"
],
"title": "Extending Granger causality to nonlinear systems",
"url": "https://arxiv.org/abs/physics/0405009"
},
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