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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15759
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dc.contributor.authorRodriguez Rivero, Cristian-
dc.contributor.authorTúpac Valdivia, Yván Jesús-
dc.contributor.authorPucheta, Julian-
dc.contributor.authorJuarez, Gustavo-
dc.contributor.authorFranco, Leonardo-
dc.contributor.authorOtaño, Paula-
dc.date.accessioned2019-01-29T22:19:49Z-
dc.date.available2019-01-29T22:19:49Z-
dc.date.issued2018-
dc.identifier.urihttp://repositorio.ucsp.edu.pe/handle/UCSP/15759-
dc.description.abstractThis paper presents a new method to forecast short rainfall time-series. The new framework is by means of Bayesian enhanced modified combined approach (BEMCA) using permutation and relative entropy with Bayesian inference. The aim at the proposed filter is focused on short datasets consisting of at least 36 samples. The structure of the artificial neural networks (ANNs) change according to data model selected, such as the Bayesian approach can be combined with the entropic information of the series. Then computational results are assessed on time series competition and rainfall series, afterwards they are compared with ANN nonlinear approaches proposed in recent work and naïve linear technique such us ARMA. To show a better performance of BEMCA filter, results are analyzed in their forecast horizons by SMAPE and RMSE indices. BEMCA filter shows an increase of accuracy in 3-6 prediction horizon analyzing the dynamic behavior of chaotic series for short series predictions. © 2017 IEEE.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.language.isoenges_PE
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85050411038&doi=10.1109%2fLA-CCI.2017.8285721&partnerID=40&md5=160787230ff2415755ec49a424cd0066es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectBayesian networkses_PE
dc.subjectEntropyes_PE
dc.subjectForecastinges_PE
dc.subjectInference engineses_PE
dc.subjectNeural networkses_PE
dc.subjectTime serieses_PE
dc.subjectBayesianes_PE
dc.subjectBayesian approacheses_PE
dc.subjectBayesian inferencees_PE
dc.subjectComputational resultses_PE
dc.subjectPermutation entropyes_PE
dc.subjectRelative entropyes_PE
dc.subjectShort time serieses_PE
dc.subjectTime series predictiones_PE
dc.subjectRaines_PE
dc.titleTime-series prediction with BEMCA approach: Application to short rainfall serieses_PE
dc.typeinfo:eu-repo/semantics/conferenceObjectes_PE
dc.identifier.doi10.1109/LA-CCI.2017.8285721es_PE
Appears in Collections:Artículos de investigación

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