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dc.contributor.authorÖzdemir, Özer
dc.contributor.authorMemmedli, Memmedaga
dc.contributor.editorAidaZade, K
dc.date.accessioned2019-10-18T18:43:36Z
dc.date.available2019-10-18T18:43:36Z
dc.date.issued2012
dc.identifier.isbn978-1-4673-4502-6
dc.identifier.urihttps://hdl.handle.net/11421/10346
dc.description4th International Conference on Problems of Cybernetics and Informatics (PCI) -- SEP 12-14, 2012 -- Baku, AZERBAIJANen_US
dc.descriptionWOS: 000320337500196en_US
dc.description.abstractFuzzy time series models have become important in past decades with neural networks. Hence, this study aims to improve forecasting performance of neural network based fuzzy time series by using an optimization function to interval length which affects forecasting accuracy. So, a new approach for improving forecasting performance of neural network-based fuzzy time series is applied with optimization process. The empirical results show that the model with proposed approach by optimization of interval length outperforms other forecasting models proposed in the literature.en_US
dc.description.sponsorshipIEEEen_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFuzzy Time Seriesen_US
dc.subjectNeural Networksen_US
dc.subjectOptimizationen_US
dc.subjectForecastingen_US
dc.subjectInterval Lengthen_US
dc.titleOptimization of Interval Length for Neural Network Based Fuzzy Time Seriesen_US
dc.typeconferenceObjecten_US
dc.relation.journal2012 Iv International Conference Problems of Cybernetics and Informatics (Pci)en_US
dc.contributor.departmentAnadolu Üniversitesien_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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