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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15812
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dc.contributor.authorRodriguez Rivero, Cristian-
dc.contributor.authorPucheta, Julian-
dc.contributor.authorOrjuela Canon, Alvaro-
dc.contributor.authorFranco, Leonardo-
dc.contributor.authorTúpac Valdivia, Yván Jesús-
dc.contributor.authorOtano, Paula-
dc.contributor.authorSauchelli, V.-
dc.date.accessioned2019-01-29T22:19:52Z-
dc.date.available2019-01-29T22:19:52Z-
dc.date.issued2017-
dc.identifier.issn15480992es_PE
dc.identifier.urihttp://repositorio.ucsp.edu.pe/handle/UCSP/15812-
dc.description.abstractThis article proposes that the combination of smoothing approach considering the entropic information provided by Renyi's method, has an acceptable performance in term of forecasting errors. The methodology of the proposed scheme is examined through benchmark chaotic time series, such as Mackey Glass, Lorenz, Henon maps, the Lynx and rainfall from Santa Francisca-Cordoba, with addition of white noise by using neural networks-based energy associated (EAS) predictor filter modified by Renyi's entropy of the series. When the time series is short or long, the underlying dynamical system is nonlinear and temporal dependencies span long time intervals, in which this are also called long memory process. In such cases, the inherent nonlinearity of neural networks models and a higher robustness to noise seem to partially explain their better prediction performance when entropic information is extracted from the series. Then, to demonstrate that permutation entropy is computationally efficient, robust to outliers, and effective to measure complexity of time series, computational results are evaluated against several non-linear ANN predictors to show the predictability of noisy rainfall and chaotic time series reported in the literature. © 2003-2012 IEEE.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.language.isoenges_PE
dc.publisherIEEE Computer Societyes_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85022194082&doi=10.1109%2fTLA.2017.7959353&partnerID=40&md5=4ff444292907841eb800f6f39d7a0722es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectComplex networkses_PE
dc.subjectDynamical systemses_PE
dc.subjectEnterprise softwarees_PE
dc.subjectEntropyes_PE
dc.subjectForecastinges_PE
dc.subjectNeural networkses_PE
dc.subjectTime serieses_PE
dc.subjectWhite noisees_PE
dc.subjectAcceptable performancees_PE
dc.subjectChaotic time serieses_PE
dc.subjectChaotic time series forecastes_PE
dc.subjectComputational resultses_PE
dc.subjectComputationally efficientes_PE
dc.subjectenergy associated to series (EAS)es_PE
dc.subjectPrediction performancees_PE
dc.subjectRenyi's entropic informationes_PE
dc.subjectRaines_PE
dc.titleNoisy Chaotic time series forecast approximated by combining Reny's entropy with Energy associated to series method: Application to rainfall serieses_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.doi10.1109/TLA.2017.7959353es_PE
Appears in Collections:Artículos de investigación

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