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A Machine Learning Study to Enhance Project Cost Forecasting

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dc.contributor.author İnan, Tolga
dc.contributor.author Narbaev, Timur
dc.contributor.author Hazır, Öncü
dc.date.accessioned 2024-03-05T13:01:07Z
dc.date.available 2024-03-05T13:01:07Z
dc.date.issued 2022
dc.identifier.citation İnan, Tolga; Narbaev, Timur; Hazır, Öncü (2022). "A Machine Learning Study to Enhance Project Cost Forecasting", IFAC-PapersOnLine, Vol. 55, No. 10, pp. 3286-3291. tr_TR
dc.identifier.uri http://hdl.handle.net/20.500.12416/7483
dc.description.abstract In project management it is critical to obtain accurate cost forecasts using effective methods. This study presents a Machine Learning model based on Long-Short Term Memory to forecast the project cost. The model uses the seven-dimensional feature vector, including schedule and cost performance factors and their moving averages as a predictor. Based on the cost variation patterns from the training phase, we validate the model using three hundred experiments in the testing phase. Overall, the proposed model produces more accurate cost estimates when compared to the traditional Earned Value Management index-based model. tr_TR
dc.language.iso eng tr_TR
dc.publisher elsevier tr_TR
dc.relation.isversionof 10.1016/j.ifacol.2022.10.127 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Cost Forecasting tr_TR
dc.subject Earned Value Management tr_TR
dc.subject Estimate at Completion tr_TR
dc.subject Machine Learning tr_TR
dc.subject Project Management tr_TR
dc.title A Machine Learning Study to Enhance Project Cost Forecasting tr_TR
dc.type article tr_TR
dc.relation.journal IFAC-PapersOnLine tr_TR
dc.identifier.volume 55 tr_TR
dc.identifier.issue 10 tr_TR
dc.identifier.startpage 3286 tr_TR
dc.identifier.endpage 3291 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü tr_TR


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