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dc.contributor.authorAydın, Dursun
dc.contributor.authorMammadov, Mammadagha
dc.date.accessioned2019-10-20T09:31:21Z
dc.date.available2019-10-20T09:31:21Z
dc.date.issued2014
dc.identifier.issn1012-9367
dc.identifier.urihttps://hdl.handle.net/11421/17673
dc.descriptionWOS: 000340085300002en_US
dc.description.abstractThe focus in this paper embraces the hybrid models whose components are nonparametric regression and artificial neural networks. Smoothing spline, regression spline and additive regression models are considered as the nonparametric regression components. Furthermore, various multilayer perceptron algorithms and radial basis function network model are regarded as the artificial neural networks components. The performances of these models are compared by forecasting three real Turkish data sets: Domestic product per capita (GDP), the number of cars produced and the number of tourist arrivals. The results obtained by experimental evaluations show that hybrid models proposed in this paper have performed much better in comparison to hybrid models discussed in literature.en_US
dc.language.isoengen_US
dc.publisherIsoss Publen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHybrid Modelsen_US
dc.subjectMultilayer Perceptronsen_US
dc.subjectRadial Basis Functionen_US
dc.subjectRegression Splineen_US
dc.subjectSmoothing Splineen_US
dc.titleTime Series Forecasting Using a Hybrid Neural Networks and Nonparametric Regression Modelen_US
dc.typearticleen_US
dc.relation.journalPakistan Journal of Statisticsen_US
dc.contributor.departmentAnadolu Üniversitesi, Fen Fakültesi, İstatistik Bölümüen_US
dc.identifier.volume30en_US
dc.identifier.issue3en_US
dc.identifier.startpage319en_US
dc.identifier.endpage332en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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