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Discriminating chaotic time series with visibility graph eigenvalues

TitleDiscriminating chaotic time series with visibility graph eigenvalues
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2012
AuthorsFioriti, Vincenzo, Tofani A., and Di Pietro A.
JournalComplex Systems
Volume21
Pagination193-200
ISSN08912513
KeywordsAdjacency 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.

Notes

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