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dc.contributor.authorAladag C.H.
dc.contributor.authorEğrioğlu E.
dc.date.accessioned2020-06-21T09:28:37Z
dc.date.available2020-06-21T09:28:37Z
dc.date.issued2012
dc.identifier.isbn9.78161E+12
dc.identifier.urihttps://doi.org/10.2174/978160805373511201010003
dc.identifier.urihttps://hdl.handle.net/20.500.12712/4346
dc.description.abstractThe researchers from various fields have been studying on time series forecasting for approximately one century in order to get better forecasts for the future.To achieve high forecast accuracy level, various time series forecasting approaches have been improved in the literature. During 1980s, some crucial developments happened and time series researches changed. More sophisticated algorithms could be improved since properties of computers were enhanced. Therefore, new time series forecasting approaches such as artificial neural networks and fuzzy time series could be proposed. In the applications, these approaches have proved its success in forecasting real life time series. In addition, hybrid forecasting methods which combine these new approaches have also been improved to obtain more accurate forecasts. In recent years, these advanced time series forecasting methods have been used to forecast real life time series and satisfactory results have also been obtained. © 2012 Bentham Science Publishers. All rights reserved.en_US
dc.language.isoengen_US
dc.publisherBentham Science Publishers Ltd.en_US
dc.relation.isversionof10.2174/978160805373511201010003en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectForecastingen_US
dc.subjectFuzzy time seriesen_US
dc.subjectHybrid methodsen_US
dc.titleAdvanced time series forecasting methodsen_US
dc.typebookParten_US
dc.contributor.departmentOMÜen_US
dc.identifier.startpage3en_US
dc.identifier.endpage10en_US
dc.relation.journalAdvances in Time Series Forecastingen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US


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