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dc.contributor.authorDlask M.
dc.contributor.authorKukal J.
dc.date.accessioned2020-03-08T21:11:54Z
dc.date.available2020-03-08T21:11:54Z
dc.date.issued2019
dc.identifierV3S-335999
dc.identifier.citationDLASK, M. and J. KUKAL. Hurst exponent estimation from short time series. SIGNAL IMAGE AND VIDEO PROCESSING. 2019, 13(2), 263-269. ISSN 1863-1703. DOI 10.1007/s11760-018-1353-2.
dc.identifier.issn1863-1703 (print)
dc.identifier.urihttp://hdl.handle.net/10467/86994
dc.description.abstractFractal investigation of time series is very complex for several reasons. Due to the existence of fully continuous model, on which the majority of conventional methods are based, the quality of Hurst exponent estimate is often influenced by the number of input data and its sampling rate. In this work, we present a novel approach of unbiased Hurst exponent estimate that is suitable especially for short time series. The crucial idea is deriving the discrete fractional Brownian bridge and its statistical properties that can be subsequently used for model parameter estimation. For the verification and demonstration of efficiency of the method, several generators of fractional Gaussian noise are presented and tested.eng
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofSIGNAL IMAGE AND VIDEO PROCESSING
dc.subjectHurst exponenteng
dc.subjectFractional Brownian motioneng
dc.subjectFractional Gaussian noiseeng
dc.subjectShort time serieseng
dc.subjectFractal dimensioneng
dc.titleHurst exponent estimation from short time serieseng
dc.typečlánek v časopisecze
dc.typearticleeng
dc.identifier.doi10.1007/s11760-018-1353-2
dc.rights.accessrestrictedAccess
dc.identifier.wos000459989400007
dc.type.statusPeer-reviewed
dc.type.versionpublishedVersion
dc.identifier.scopus2-s2.0-85053269788


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