A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model

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dc.authorid0000-0003-4301-4149
dc.contributor.authorEgrioglu, Erol
dc.contributor.authorAladag, Cagdas Hakan
dc.contributor.authorYolcu, Ufuk
dc.contributor.authorBasaran, Murat A.
dc.contributor.authorUslu, Vedide R.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2009
dc.departmentNiğde ÖHÜ
dc.description.abstractIn the literature, there have been many studies using fuzzy time series for the purpose of forecasting. The most studied model is the first order fuzzy time series model. In this model, an observation of fuzzy time series is obtained by using the previous observation. In other words, only the first tagged variable is used when constructing the first order fuzzy time series model. Therefore, this model can not be sufficient for some time series such as seasonal time series which is an important class in time series models. Besides, the time series encountered in real life have not only autoregressive (AR) structure but also moving average (MA) structure. The fuzzy time series models available in the literature are AR structured and are not appropriate for MA structured time series. In this paper, a hybrid approach is proposed in order to analyze seasonal fuzzy time series. The proposed hybrid approach is based on partial high order bivariate fuzzy time series forecasting model which is first introduced in this paper. The order of this model is determined by utilizing Box-Jenkins method. In order to show the efficiency of the proposed hybrid method, real time series are analyzed with this method. The results obtained from the proposed method are compared with the other methods. As a result, it is observed that more accurate results are obtained from the proposed hybrid method. (C) 2008 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.eswa.2008.09.040
dc.identifier.endpage7434
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue4
dc.identifier.scopus2-s2.0-60249083330
dc.identifier.scopusqualityQ1
dc.identifier.startpage7424
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2008.09.040
dc.identifier.urihttps://hdl.handle.net/11480/5061
dc.identifier.volume36
dc.identifier.wosWOS:000264528600011
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBivariate
dc.subjectBox-Jenkins method
dc.subjectFeed forward neural networks
dc.subjectForecasting
dc.subjectHigh order
dc.subjectSeasonal fuzzy time series
dc.titleA new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model
dc.typeArticle

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