Acceleration of time series entropy algorithms

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dc.contributor.authorTomčala, Jiří
dc.date.accessioned2019-05-13T12:06:03Z
dc.date.available2019-05-13T12:06:03Z
dc.date.issued2019
dc.description.abstractThis paper concentrates on the entropy estimation of time series. Two new algorithms are introduced: Fast Approximate Entropy and Fast Sample Entropy. Their main advantage is their lower time complexity. Examples considered in the paper include interesting experiments with real-world data obtained from IT4Innovations' supercomputers Salomon and Anselm, as well as with data artificially created specifically to test the credibility of these new entropy analyzers.cs
dc.description.firstpage1443cs
dc.description.issue3cs
dc.description.lastpage1454cs
dc.description.sourceWeb of Sciencecs
dc.description.volume75cs
dc.identifier.citationJournal of Supercomputing. 2019, vol. 75, issue 3, special issue, p. 1443-1454.cs
dc.identifier.doi10.1007/s11227-018-2657-2
dc.identifier.issn0920-8542
dc.identifier.issn1573-0484
dc.identifier.urihttp://hdl.handle.net/10084/134901
dc.identifier.wos000463635700034
dc.language.isoencs
dc.publisherSpringercs
dc.relation.ispartofseriesJournal of Supercomputingcs
dc.relation.urihttps://doi.org/10.1007/s11227-018-2657-2cs
dc.rights© Springer Science+Business Media, LLC, part of Springer Nature 2018cs
dc.subjectentropycs
dc.subjectapproximate entropycs
dc.subjectsample entropycs
dc.subjectFast Approximate Entropycs
dc.subjectFast Sample Entropycs
dc.titleAcceleration of time series entropy algorithmscs
dc.typearticlecs
dc.type.statusPeer-reviewedcs

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