A new approach based on artificial neural networks for high order multivariate fuzzy time series

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dc.authorid0000-0003-4301-4149
dc.contributor.authorEgrioglu, Erol
dc.contributor.authorAladag, Cagdas Hakan
dc.contributor.authorYolcu, Ufuk
dc.contributor.authorUslu, Vedide R.
dc.contributor.authorBasaran, Murat A.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2009
dc.departmentNiğde ÖHÜ
dc.description.abstractFuzzy time series methods have been recently becoming very popular in forecasting. These methods can be categorized into two subclasses that are univariate and multivariate approaches. It is a known fact that real time series data can actually be affected by many factors. In this case, the using multivariate fuzzy time series forecasting model can be more reasonable in order to get more accurate forecasts. To obtain fuzzy forecasts when multivariate fuzzy time series approach is adopted, the most applied method is using tables of fuzzy relations. However, employing this method is a computationally though task. In this study, we introduce a new method that does not require using fuzzy logic relation tables in order to determine fuzzy relationships. Instead, a feed forward artificial neural network is employed to determine fuzzy relationships. The proposed method is applied to the time series data of the total number of annual car road accidents casualties in Belgium from 1974 to 2004 and a comparison is made between our proposed method and the methods proposed by Jilani and Burney [Jilani, T. A., & Burney, S. M. A. (2008). Multivariate stochastic fuzzy forecasting models. Expert Systems with Applications, 35, 691-700] and Lee et al. [Lee, L.-W., Wang, L.-H., Chen, S.-M., & Leu, Y.-H. (2006). Handling forecasting problems based on two factors high order fuzzy time series. IEEE Transactions on Fuzzy Systems, 14, 468-477]. (C) 2009 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.eswa.2009.02.057
dc.identifier.endpage10594
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue7
dc.identifier.scopus2-s2.0-67349187003
dc.identifier.scopusqualityQ1
dc.identifier.startpage10589
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2009.02.057
dc.identifier.urihttps://hdl.handle.net/11480/5025
dc.identifier.volume36
dc.identifier.wosWOS:000266851000044
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.subjectArtificial neural networks
dc.subjectForecasting
dc.subjectFuzzy time series
dc.subjectMultivariate fuzzy time series approaches
dc.titleA new approach based on artificial neural networks for high order multivariate fuzzy time series
dc.typeArticle

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