Conditional dispersion to detect causality between chaotic time series

Versione 1.0.0.0 (3,45 KB) da Dmitry Volobuev
Causality detection using calculation of distances in Takens's spaces
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Aggiornato 10 gen 2017

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the package contains files for testing a conditional dispersion method
to detect causality relations between time series
-cd_tst_henon_map.m runs the test based on coupled Henon maps
-henon_map.m - equations of coupled Henon maps by Cenys et al.,(1991)

-cd_tst_VdP.m runs the test based on coupled Van der Pol equations.
-VdP.m - function to calculate coupled Van der Pol equations (dos Santos et al., 2004)

-nlincor.m - the basic function to calculate conditional dispersion by Cenys et al.,(1991)

-taks.m is the function to calculate the Takens's space from the observable time series.

dos Santos, A. M., Lopes, S. R., Viana, R. R. L.: 2004, Rhythm synchronization and chaotic modulation of coupled Van der Pol oscillators in a model for the heartbeat. Physica A: Statistical Mechanics and its Applications, 338(3), 335-355.

Cenys, A., Lasiene, G., Pyragas, K.:1991, Estimation of interrelation between chaotic observables. Physica D: Nonlinear Phenomena, 52(2), 332-337.

Cita come

Dmitry Volobuev (2024). Conditional dispersion to detect causality between chaotic time series (https://www.mathworks.com/matlabcentral/fileexchange/61076-conditional-dispersion-to-detect-causality-between-chaotic-time-series), MATLAB Central File Exchange. Recuperato .

Compatibilità della release di MATLAB
Creato con R2015b
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Ispirato da: convergent cross mapping

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Versione Pubblicato Note della release
1.0.0.0