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dc.contributor.authorAladag, Cagdas Hakan
dc.contributor.authorEgrioglu, Erol
dc.contributor.authorKadilar, Cem
dc.date.accessioned2019-12-16T08:35:19Z
dc.date.available2019-12-16T08:35:19Z
dc.date.issued2009
dc.identifier.issn0893-9659
dc.identifier.urihttps://doi.org/10.1016/j.aml.2009.02.006
dc.identifier.urihttp://hdl.handle.net/11655/19544
dc.description.abstractIn recent years, artificial neural networks (ANNs) have been used for forecasting in time series in the literature. Although it is possible to model both linear and nonlinear structures in time series by using ANNs, they are not able to handle both structures equally well. Therefore, the hybrid methodology combining ARIMA and ANN models have been used in the literature. In this study, a new hybrid approach combining Elman's Recurrent Neural Networks (ERNN) and ARIMA models is proposed. The proposed hybrid approach is applied to Canadian Lynx data and it is found that the proposed approach has the best forecasting accuracy. (c) 2009 Elsevier Ltd, All rights reserved.
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.isversionof10.1016/j.aml.2009.02.006
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMathematics
dc.titleForecasting Nonlinear Time Series With A Hybrid Methodology
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.relation.journalApplied Mathematics Letters
dc.contributor.departmentİstatistik
dc.identifier.volume22
dc.identifier.issue9
dc.identifier.startpage1467
dc.identifier.endpage1470
dc.description.indexWoS
dc.description.indexScopus


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