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dc.contributor.authorIşıkdağ, Ümit
dc.contributor.authorHepsağ, Aycan
dc.contributor.authorİmre Bıyıklı, Süreyya
dc.contributor.authorÖz, Derya
dc.contributor.authorBekdaş, Gebrail
dc.contributor.authorGeem, Zong Woo
dc.date.accessioned2023-03-27T09:18:14Z
dc.date.available2023-03-27T09:18:14Z
dc.date.issued2023en_US
dc.identifier.issn1546-2218
dc.identifier.issn1546-2226
dc.identifier.urihttps://hdl.handle.net/11363/4239
dc.description.abstractConstruction Industry operates relying on various key economic indicators. One of these indicators is material prices. On the other hand, cost is a key concern in all operations of the construction industry. In the uncertain conditions, reliable cost forecasts become an important source of information. Material cost is one of the key components of the overall cost of construction. In addition, cost overrun is a common problem in the construction industry, where nine out of ten construction projects face cost overrun. In order to carry out a successful cost management strategy and prevent cost overruns, it is very important to find reliable methods for the estimation of construction material prices. Material prices have a time dependent nature. In order to increase the foreseeability of the costs of construction materials, this study focuses on estimation of construction material indices through time series analysis. Two different types of analysis are implemented for estimation of the future values of construction material indices. The first method implemented was Autoregressive Integrated Moving Average (ARIMA), which is known to be successful in estimation of time series having a linear nature. The second method implemented was Non-Linear Autoregressive Neural Network (NARNET) which is known to be successful in modeling and estimating of series with non-linear components. The results have shown that depending on the nature of the series, both these methods can successfully and accurately estimate the future values of the indices. In addition, we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.en_US
dc.language.isoengen_US
dc.publisherTECH SCIENCE PRESS, 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052en_US
dc.relation.isversionof10.32604/cmc.2023.032502en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectConstruction material indicesen_US
dc.subjectARIMAen_US
dc.subjectnon-linear autoregressive neural networken_US
dc.subjectNARNETsen_US
dc.titleEstimating Construction Material Indices with ARIMA and Optimized NARNETsen_US
dc.typearticleen_US
dc.relation.ispartofComputers, Materials & Continuaen_US
dc.departmentİktisadi İdari ve Sosyal Bilimler Fakültesien_US
dc.identifier.volume74en_US
dc.identifier.issue1en_US
dc.identifier.startpage113en_US
dc.identifier.endpage129en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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