Basit öğe kaydını göster

dc.contributor.authorAladag C.H.
dc.contributor.authorEğrioğlu E.
dc.date.accessioned2020-06-21T09:28:36Z
dc.date.available2020-06-21T09:28:36Z
dc.date.issued2012
dc.identifier.isbn9.78161E+12
dc.identifier.urihttps://doi.org/10.2174/978160805373511201010118
dc.identifier.urihttps://hdl.handle.net/20.500.12712/4342
dc.description.abstractFuzzy time series approaches have been recently used for forecasting in many studies [1]. These approaches can be categorized into two subclasses that are univariate and multivariate approaches. It is a fact that many factors can actually affect real time series data. Therefore, using a multivariate fuzzy time series forecasting model can be more reasonable in order to get more accurate forecasts. The most preferred method is using tables of fuzzy relations for determining fuzzy relations in multivariate fuzzy time series approaches in the literature. However, employing this method is a computationally though task. In this study, we propose a new method based on utilizing artificial neural networks in determining fuzzy logic relations and using the formula defined by Jilani and Burney [2] in calculating defuzzyfied forecasts. Hence, it is aimed to produce more accurate forecasts and avoid intense computations. 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 [2] and Lee et al. [3]. © 2012 Bentham Science Publishers. All rights reserved.en_US
dc.language.isoengen_US
dc.publisherBentham Science Publishers Ltd.en_US
dc.relation.isversionof10.2174/978160805373511201010118en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectForecastingen_US
dc.subjectFuzzy time seriesen_US
dc.subjectMultivariate fuzzy time series approachesen_US
dc.titleA hybrid forecasting model based on multivariate fuzzy time series and artificial neural networksen_US
dc.typebookParten_US
dc.contributor.departmentOMÜen_US
dc.identifier.startpage118en_US
dc.identifier.endpage129en_US
dc.relation.journalAdvances in Time Series Forecastingen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US


Bu öğenin dosyaları:

DosyalarBoyutBiçimGöster

Bu öğe ile ilişkili dosya yok.

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster