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dc.contributor.authorAydın, Dursun
dc.contributor.authorMammadov, Mammadagha
dc.description.abstractThis paper presents a comparative study of the hybrid models, neural networks and nonparametric regression models in time series forecasting. The components of these hybrid models are consisting of the nonparametric regression and artificial neural networks models. 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 the series of number of produced Cars and Domestic product per capita (GDP) data occurred in Turkey. This comparisons show that hybrid models proposed in this paper have denoted much more excellent performance than the hybrid models in literature.en_US
dc.subjectAdditive Regression Modelen_US
dc.subjectHybrid Modelsen_US
dc.subjectMultilayer Perceptronsen_US
dc.subjectNeural Networksen_US
dc.subjectNonparametric Regressionen_US
dc.subjectRadial Basis Functionen_US
dc.subjectTime Seriesen_US
dc.titleA comparative study of hybrid, neural networks and nonparametric regression models in time series predictionen_US
dc.relation.journalWSEAS Transactions on Mathematicsen_US
dc.contributor.departmentAnadolu Üniversitesi, Fen Fakültesi, İstatistik Bölümüen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US

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