A new fuzzy time series method based on an ARMA-type recurrent Pi-Sigma artificial neural network

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dc.authoridKoçak, Cem / 0000-0002-7339-7438
dc.authoridBaş, Eren / 0000-0002-0263-8804
dc.authoridEğrioğlu, Erol / 0000-0003-4301-4149
dc.authorwosidKoçak, Cem / AAP-6074-2021
dc.authorwosidEğrioğlu, Erol / AAE-4706-2019
dc.contributor.authorKoçak, Cem
dc.contributor.authorDalar, Ali Zafer
dc.contributor.authorCağcağ Yolcu, Özge
dc.contributor.authorBaş, Eren
dc.contributor.authorEğrioğlu, Erol
dc.date.accessioned2021-11-01T15:05:06Z
dc.date.available2021-11-01T15:05:06Z
dc.date.issued2020
dc.departmentHitit Üniversitesi, Sağlık Bilimleri Fakültesi, Hemşirelik Bölümü
dc.description.abstractAs it known in many studies, the fuzzy time series methods do not need assumptions such as stationary and the linearity required for classical time series approaches, so there is a huge field of study on fuzzy time series methods in the time series literature. Fuzzy time series literature has the studies which use both the various models of artificial neural networks and the different optimization methods of artificial intelligence jointly. In this study, a new fuzzy time series algorithm based on an ARMA-type recurrent Pi-Sigma artificial neural network is introduced. It is expected that the proposed method increases the forecasting performance for many real-life time series because of using more input which is the error term obtained from Pi-Sigma artificial neural network with recurrent structure. Therefore, it can be considered that the proposed method is based on an ARMA-type fuzzy time series forecasting model. In the proposed method, the training of recurrent ARMA-type Pi-Sigma neural network is performed by particle swarm optimization. The proposed method has been applied to a real-data set as well as simulated data sets of a real-life time series, and the obtained results have been compared with some other methods in the literature.
dc.identifier.citationKocak, C., Dalar, A. Z., Cagcag Yolcu, O., Bas, E., & Egrioglu, E. (2020). A new fuzzy time series method based on an ARMA-type recurrent Pi-Sigma artificial neural network. Soft Computing, 24(11), 8243-8252.
dc.identifier.doi10.1007/s00500-019-04506-1
dc.identifier.endpage8252en_US
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85075189636
dc.identifier.scopusqualityQ1
dc.identifier.startpage8243en_US
dc.identifier.urihttps://doi.org/10.1007/s00500-019-04506-1
dc.identifier.urihttps://hdl.handle.net/11491/7115
dc.identifier.volume24en_US
dc.identifier.wosWOS:000495711400002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKoçak, Cem
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofSoft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFuzzy Time Seriesen_US
dc.subjectRecurrent Pi-Sigma Artificial Neural Networken_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectARMA-type Fuzzy Time Seriesen_US
dc.subjectForecastingen_US
dc.titleA new fuzzy time series method based on an ARMA-type recurrent Pi-Sigma artificial neural network
dc.typeArticle

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