ARMA(p,q) type high order fuzzy time series forecast method based on fuzzy logic relations

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dc.contributor.authorKoçak, Cem
dc.date.accessioned2019-05-13T09:04:47Z
dc.date.available2019-05-13T09:04:47Z
dc.date.issued2017
dc.departmentHitit Üniversitesi, Sağlık Bilimleri Fakültesi, Hemşirelik Bölümü
dc.description.abstractWithin classic time series approaches, a time series model can be studied under 3 groups, namely AR (autoregressive model), MA (moving averages model) and ARMA (autoregressive moving averages model). On the other hand, solutions are based mostly on fuzzy AR time series models in the fuzzy time series literature. However, just a few fuzzy ARMA time series models have proposed until now. Fuzzy AR time series models have been divided into two groups named first order and high order models in the literature, highlighting the impact of model degree on forecast performance. However, model structure has been disregarded in these fuzzy AR models. Therefore, it is necessary to eliminate the model specification error arising from not utilizing of MA variables in the fuzzy time series approaches. For this reason, a new high order fuzzy ARMA(p,q) time series solution algorithm based on fuzzy logic group relations including fuzzy MA variables along with fuzzy AR variables has been proposed in this study. The main purpose of this article is to show that the forecast performance can be significantly improved when the deficiency of not utilizing MA variables. The other aim is also to show that the proposed method is better than the other fuzzy ARMA time series models in the literature from the point of forecast performance. Therefore, the new proposed method has been compared regarding forecast performance against some methods commonly used in literature by applying them on gold prices in Turkey, Istanbul Stock Exchange (IMKB) and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). © 2017 Elsevier B.V.
dc.identifier.citationKoçak, C. (2017). ARMA (p, q) type high order fuzzy time series forecast method based on fuzzy logic relations. Applied Soft Computing, 58, 92-103.
dc.identifier.doi10.1016/j.asoc.2017.04.021
dc.identifier.endpage103en_US
dc.identifier.issn1568-4946
dc.identifier.scopusqualityQ1
dc.identifier.startpage92en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2017.04.021
dc.identifier.urihttps://hdl.handle.net/11491/1642
dc.identifier.volume58en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofApplied Soft Computing Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFuzzy ARMA Modelsen_US
dc.subjectFuzzy Autoregressive – Moving Avarage Modelen_US
dc.subjectFuzzy Time Seriesen_US
dc.subjectGroup Relation Tableen_US
dc.subjectHigh Order Fuzzy Time Seriesen_US
dc.titleARMA(p,q) type high order fuzzy time series forecast method based on fuzzy logic relations
dc.typeArticle

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