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Discriminating chaotic time series with visibility graph eigenvalues

TitoloDiscriminating chaotic time series with visibility graph eigenvalues
Tipo di pubblicazioneArticolo su Rivista peer-reviewed
Anno di Pubblicazione2012
AutoriFioriti, Vincenzo, Tofani A., and Di Pietro A.
RivistaComplex Systems
Volume21
Paginazione193-200
ISSN08912513
Parole chiaveAdjacency matrices, Chaotic time series, Eigen-value, Eigenvalues and eigenfunctions, Gross domestic products, Horizontal visibility graphs, Short time series, Time series, Visibility graphs
Abstract

Time series can be transformed into graphs called horizontal visibility graphs (HVGs) in order to gain useful insights. Here, the maximum eigenvalue of the adjacency matrix associated to the HVG derived from several time series is calculated. The maximum eigenvalue methodology is able to discriminate between chaos and randomness and is suitable for short time series, hence for experimental results. An application to the United States gross domestic product data is given. © 2012 Complex Systems Publications, Inc.
Note

cited By 2
URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84875357101&partnerID=40&md5=be67fdeb5bb1718df557c876cf753068
Citation KeyFioriti2012193