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dc.contributor.authorMutua, Stephen
dc.contributor.authorChanggui, Gu
dc.contributor.authorHuijie, Yang
dc.date.accessioned2021-10-07T12:47:36Z
dc.date.available2021-10-07T12:47:36Z
dc.date.issued2016
dc.identifier.citationMutua, S., Gu, C., & Yang, H. (2016). Visibility graphlet approach to chaotic time series. Chaos (Woodbury, N.Y.), 26(5), 053107. https://doi.org/10.1063/1.4951681en_US
dc.identifier.urihttp://repository.must.ac.ke/handle/123456789/400
dc.description.abstractMany novel methods have been proposed for mapping time series into complex networks. Although some dynamical behaviors can be effectively captured by existing approaches, the preservation and tracking of the temporal behaviors of a chaotic system remains an open problem. In this work, we extended the visibility graphlet approach to investigate both discrete and continuous chaotic time series. We applied visibility graphlets to capture the reconstructed local states, so that each is treated as a node and tracked downstream to create a temporal chain link. Our empirical findings show that the approach accurately captures the dynamical properties of chaotic systems. Networks constructed from periodic dynamic phases all converge to regular networks and to unique network structures for each model in the chaotic zones. Furthermore, our results show that the characterization of chaotic and non-chaotic zones in the Lorenz system corresponds to the maximal Lyapunov exponent, thus providing a simple and straightforward way to analyze chaotic systems.en_US
dc.language.isoenen_US
dc.publisherAIP Publishing LLCen_US
dc.titleVisibility Graphlet Approach to Chaotic Time Seriesen_US
dc.typeArticleen_US


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