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Fuzzy Clustering To Classify Several Time Series Models With Fractional Brownian Motion Errors

dc.contributor.author Baleanu, Dumitru
dc.contributor.author Qasem, Sultan Noman
dc.contributor.author Mosavi, Amirhosein
dc.contributor.author Band, Shahab S.
dc.contributor.author Mahmoudi, Mohammad Reza
dc.contributor.authorID 56389 tr_TR
dc.date.accessioned 2022-05-11T10:52:44Z
dc.date.accessioned 2025-09-18T16:07:31Z
dc.date.available 2022-05-11T10:52:44Z
dc.date.available 2025-09-18T16:07:31Z
dc.date.issued 2021
dc.description S. Band, Shahab/0000-0001-6109-1311; Noman Qasem, Sultan/0000-0002-6575-161X en_US
dc.description.abstract In real world problems, scientists aim to classify and cluster several time series processes that can be used for a dataset. In this research, for the first time, based on fuzzy clustering method, an approach is applied to classify and cluster several time series models with fractional Brownian motion errors as candidates to fit on a dataset. The ability of the introduced technique is studied using simulation and real world example. (C) 2020 The Authors. Published by Elsevier B.V. en_US
dc.description.publishedMonth 2
dc.identifier.citation Mahmoudi, Mohammad Reza...et al. (2021). "Fuzzy clustering to classify several time series models with fractional Brownian motion errors", Alexandria Engineering Journal, Vol. 60, No. 1, pp. 1137-1145. en_US
dc.identifier.doi 10.1016/j.aej.2020.10.037
dc.identifier.issn 1110-0168
dc.identifier.issn 2090-2670
dc.identifier.scopus 2-s2.0-85094612167
dc.identifier.uri https://doi.org/10.1016/j.aej.2020.10.037
dc.identifier.uri https://hdl.handle.net/20.500.12416/14772
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Classification en_US
dc.subject Fuzzy Clustering en_US
dc.subject Fractional Brownian Motion en_US
dc.subject Non-Stationary en_US
dc.subject Stationary en_US
dc.subject Time Series en_US
dc.subject Rdi en_US
dc.title Fuzzy Clustering To Classify Several Time Series Models With Fractional Brownian Motion Errors en_US
dc.title Fuzzy clustering to classify several time series models with fractional Brownian motion errors tr_TR
dc.type Article en_US
dspace.entity.type Publication
gdc.author.id S. Band, Shahab/0000-0001-6109-1311
gdc.author.id Noman Qasem, Sultan/0000-0002-6575-161X
gdc.author.institutional Baleanu, Dumitru
gdc.author.scopusid 56684432000
gdc.author.scopusid 7005872966
gdc.author.scopusid 34771853500
gdc.author.scopusid 57191408081
gdc.author.scopusid 57221738247
gdc.author.wosid S. Band, Shahab/K-2544-2013
gdc.author.wosid Baleanu, Dumitru/B-9936-2012
gdc.author.wosid Mosavi, Amir/I-7440-2018
gdc.author.wosid Mahmoudi, Mohammad Reza/Aax-4890-2020
gdc.author.wosid S. Band, Shahab/Abb-2469-2020
gdc.author.wosid Noman Qasem, Sultan/T-2640-2017
gdc.description.department Çankaya University en_US
gdc.description.departmenttemp [Band, Shahab S.] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; [Mahmoudi, Mohammad Reza] Fasa Univ, Fac Sci, Dept Stat, Fasa, Fars, Iran; [Baleanu, Dumitru] Cankaya Univ, Fac Art & Sci, Dept Math, TR-06530 Ankara, Turkey; [Baleanu, Dumitru] Inst Space Sci, Magurele, Romania; [Qasem, Sultan Noman] Al Imam Mohammad Ibn Saud Islamic Univ IMSIU, Coll Comp & Informat Sci, Comp Sci Dept, Riyadh, Saudi Arabia; [Qasem, Sultan Noman] Taiz Univ, Fac Appl Sci, Comp Sci Dept, Taizi, Yemen; [Mosavi, Amirhosein] Ton Duc Thang Univ, Environm Qual Atmospher Sci & Climate Change Res, Ho Chi Minh City, Vietnam; [Mosavi, Amirhosein] Ton Duc Thang Univ, Fac Environm & Labour Safety, Ho Chi Minh City, Vietnam; [Band, Shahab S.] Natl Yunlin Univ Sci & Technol, Coll Future, Future Technol Res Ctr, 123 Univ Rd,Sect 3, Touliu 64002, Yunlin, Taiwan en_US
gdc.description.endpage 1145 en_US
gdc.description.issue 1 en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q1
gdc.description.startpage 1137 en_US
gdc.description.volume 60 en_US
gdc.description.woscitationindex Science Citation Index Expanded
gdc.description.wosquality Q1
gdc.identifier.openalex W4247809347
gdc.identifier.wos WOS:000605005500011
gdc.openalex.fwci 3.25106492
gdc.openalex.normalizedpercentile 0.93
gdc.openalex.toppercent TOP 10%
gdc.opencitations.count 20
gdc.plumx.crossrefcites 21
gdc.plumx.mendeley 17
gdc.plumx.scopuscites 20
gdc.scopus.citedcount 20
gdc.wos.citedcount 19
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