A new approach for determining the length of intervals for fuzzy time series

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dc.authorid0000-0003-4301-4149
dc.contributor.authorYolcu, Ufuk
dc.contributor.authorEgrioglu, Erol
dc.contributor.authorUslu, Vedide R.
dc.contributor.authorBasaran, Murat A.
dc.contributor.authorAladag, Cagdas H.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2009
dc.departmentNiğde ÖHÜ
dc.description.abstractIn the implementations of fuzzy time series forecasting, the identification of interval lengths has an important impact on the performance of the procedure. However, the interval length has been chosen arbitrarily in many papers. Huarng developed a new approach which is called ratio-based lengths of intervals in order to identify the length of intervals. In our paper, we propose a new approach which uses a single-variable constrained optimization to determine the ratio for the length of intervals. The proposed approach is applied to the two well-known time series, which are enrollment data at The University of Alabama and inventory demand data. The obtained results are compared to those of other methods. The proposed method produces more accurate predictions for the future values of used time series. (c) 2008 Elsevier B.V. All rights reserved.
dc.identifier.doi10.1016/j.asoc.2008.09.002
dc.identifier.endpage651
dc.identifier.issn1568-4946
dc.identifier.issue2
dc.identifier.scopus2-s2.0-58549116080
dc.identifier.scopusqualityQ1
dc.identifier.startpage647
dc.identifier.urihttps://dx.doi.org/10.1016/j.asoc.2008.09.002
dc.identifier.urihttps://hdl.handle.net/11480/5077
dc.identifier.volume9
dc.identifier.wosWOS:000262888100021
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherELSEVIER SCIENCE BV
dc.relation.ispartofAPPLIED SOFT COMPUTING
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFuzzy time series
dc.subjectForecasting
dc.subjectLength of interval
dc.subjectOptimization
dc.subjectFuzzy sets
dc.titleA new approach for determining the length of intervals for fuzzy time series
dc.typeArticle

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