A new deep intuitionistic fuzzy time series forecasting method based on long short?term memory

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dc.authorscopusid55128222200
dc.authorscopusid23093703600
dc.authorscopusid55927757900
dc.contributor.authorKoçak, Cem
dc.contributor.authorEğrioğlu, Erol
dc.contributor.authorBaş, Eren
dc.date.accessioned2021-01-18T09:05:41Z
dc.date.available2021-01-18T09:05:41Z
dc.date.issued2021en_US
dc.departmentHitit Üniversitesi, Sağlık Bilimleri Fakültesi, Hemşirelik Bölümü
dc.description.abstractIn recent years, deep artificial neural networks can have better forecasting performance than many other artificial neural networks. The long short-term memory (LSTM) is one of the deep artificial neural networks. There have been a few fuzzy time series forecasting model based on LSTM in the literature. However, LSTM has not been used in an intuitionistic fuzzy time series (IFTS) forecasting method until now. In this paper, determining the fuzzy relations is made by using the LSTM artificial neural network and so, a new intuitionistic fuzzy time series forecasting method based on LSTM is proposed. In the proposed method, obtaining the membership and non-membership values is performed by using intuitionistic fuzzy c-means. Then, the inputs of the LSTM are merged membership and non-membership values by a minimum operator. In this way, lagged crisp values are inputs of the long short-term memory. So, the proposed method is a high-order IFTS model. The architecture of the LSTM artificial neural network includes multiple inputs and a single output. The proposed method and some other methods in the literature are applied to the Giresun Temperature data and the Nikkei 225 stock exchange time series, and the forecasting performance of these methods is compared.
dc.description.provenanceSubmitted by Zeynep Umut Arslan (umutarslan@hitit.edu.tr) on 2021-01-18T09:04:36Z No. of bitstreams: 0en
dc.description.provenanceApproved for entry into archive by Zeynep Umut Arslan (umutarslan@hitit.edu.tr) on 2021-01-18T09:05:41Z (GMT) No. of bitstreams: 0en
dc.description.provenanceMade available in DSpace on 2021-01-18T09:05:41Z (GMT). No. of bitstreams: 0 Previous issue date: 2020en
dc.identifier.citationKocak, C., Egrioglu, E., Bas, E. (2020). A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory. The Journal of Supercomputing, 1-19.
dc.identifier.doi10.1007/s11227-020-03503-8
dc.identifier.endpage6196en_US
dc.identifier.issn0920-8542
dc.identifier.issn1573-0484
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85096451512
dc.identifier.scopusqualityQ1
dc.identifier.startpage6178en_US
dc.identifier.urihttps://hdl.handle.net/11491/5833
dc.identifier.urihttps://doi.org/10.1007/s11227-020-03503-8
dc.identifier.volume77en_US
dc.identifier.wosWOS:000591984200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKoçak, Cem
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofThe Journal of Supercomputing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectIntuitionistic Fuzzy Time Seriesen_US
dc.subjectLong Short-term Memoryen_US
dc.subjectIntuitionistic Fuzzy c-meansen_US
dc.subjectDeep Learningen_US
dc.titleA new deep intuitionistic fuzzy time series forecasting method based on long short?term memory
dc.typeArticle

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