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Bayesian Inference for Training of Long Short Term Memory Models in Chaotic Time Series Forecasting

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dc.creatorRivero C.R.spa
dc.creatorPucheta J.spa
dc.creatorPatiño D.spa
dc.creatorPuglisi J.L.spa
dc.creatorOtaño P.spa
dc.creatorFranco L.spa
dc.creatorJuarez G.spa
dc.creatorGorrostieta E.spa
dc.creatorOrjuela Cañón, Alvaro Davidspa
dc.date.accessioned2020-05-26T00:05:00Z
dc.date.available2020-05-26T00:05:00Z
dc.date.created2019spa
dc.description.abstractFor time series forecasting, obtaining models is based on the use of past observations from the same sequence. In those cases, when the model is learning from data, there is not an extra information that discuss about the quantity of noise inside the data available. In practice, it is necessary to deal with finite noisy datasets, which lead to uncertainty about the propriety of the model. For this problem, the employment of the Bayesian inference tools are preferable. A modified algorithm used for training a long-short term memory recurrent neural network for time series forecasting is presented. This approach was chosen to improve the forecasting of the original series, employing an implementation based on the minimization of the associated Kullback-Leibler Information Criterion. For comparison, a nonlinear autoregressive model implemented with a feedforward neural network was also presented. A simulation study was conducted to evaluate and illustrate results, comparing this approach with Bayesian neural-networks-based algorithms for artificial chaotic time-series and showing an improvement in terms of forecasting errors. © Springer Nature Switzerland AG 2019.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1007/978-3-030-36211-9_16
dc.identifier.issn18650929
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/23742
dc.language.isoengspa
dc.publisherSpringerspa
dc.relation.citationEndPage208
dc.relation.citationStartPage197
dc.relation.citationTitleCommunications in Computer and Information Science
dc.relation.citationVolumeVol. 1096 CCIS
dc.relation.ispartofCommunications in Computer and Information Science, ISSN:18650929, Vol.1096 CCIS,(2019); pp. 197-208spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85078452834&doi=10.1007%2f978-3-030-36211-9_16&partnerID=40&md5=157171e9c25146458ae8b1170389538cspa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordBrainspa
dc.subject.keywordFeedforward neural networksspa
dc.subject.keywordForecastingspa
dc.subject.keywordInference enginesspa
dc.subject.keywordLong short-term memoryspa
dc.subject.keywordRecurrent neural networksspa
dc.subject.keywordTime seriesspa
dc.subject.keywordTime series analysisspa
dc.subject.keywordBayesianspa
dc.subject.keywordBayesian inferencespa
dc.subject.keywordBayesian neural networksspa
dc.subject.keywordChaotic time seriesspa
dc.subject.keywordKullback-Leibler informationspa
dc.subject.keywordModified algorithmsspa
dc.subject.keywordNonlinear autoregressive modelspa
dc.subject.keywordTime series forecastingspa
dc.subject.keywordBayesian networksspa
dc.subject.keywordBayesian approximationspa
dc.subject.keywordNonlinear autoregressive modelsspa
dc.subject.keywordRecurrent neural networksspa
dc.subject.keywordTime series forecastingspa
dc.titleBayesian Inference for Training of Long Short Term Memory Models in Chaotic Time Series Forecastingspa
dc.typeconferenceObjecteng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaDocumento de conferenciaspa
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