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dc.contributor.authorWeihs, Clausen_US
dc.contributor.authorBusse, Anja M.en_US
dc.description.abstractThis paper deals with the problem of the discrimination between stable and unstable time series. One criterion for the seperation is given by the size of the Lyapunov exponent, which was originally defined for deterministic systems. However, this paper will show, that the Lyapunov exponent can also be analyzed and used for ergodic stochastic time series. Experimantal results illustrate the classification by the Lyapunov exponent. Although the Lyapunov exponent is a discriminatory parameter of the asymptotic behavior and can be interpreted as a parameter of the asymptotic distribution in the stochastic case, it has to be estimated from a given time series, where the process might still be in the transient state. Experimental results will show that in special cases the estimation leads to misclassifications of the time series and the underlying process due to the uncertainty of estimators for the Lyapunov exponent.en_US
dc.relation.ispartofseriesTechnical Report / Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen 2004,37en_US
dc.subject.stwStochastischer Prozessen_US
dc.titleLyapunov exponent for stochastic time seriesen_US
dc.typeWorking Paperen_US
Appears in Collections:Technical Reports, SFB 475: Komplexitätsreduktion in multivariaten Datenstrukturen, TU Dortmund

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