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dc.contributor.authorTayalı, Halit Alper
dc.date.accessioned2021-03-12T14:01:19Z
dc.date.available2021-03-12T14:01:19Z
dc.identifier.citationTayalı H. A. , Demand Forecasting Models With Time Series and Random Forest, "Driving Innovation and Productivity Through Sustainable Automation", Ardavan Amini,Stephen Bushell,Arshad Mahmood, Editör, IGI Global, Pennsylvania, ss.76-99, 2021
dc.identifier.otherav_2579cc89-e495-475e-816d-3348c640d7aa
dc.identifier.othervv_1032021
dc.identifier.urihttp://hdl.handle.net/20.500.12627/167748
dc.identifier.urihttps://www.igi-global.com/book/driving-innovation-productivity-through-sustainable/253848
dc.description.abstractThis chapter presents the recent methodological developments in demand management and demand forecasting subjects of the operations management. The background section provides detailed information on the domain of production management, operational analytics, and demand forecasting while providing introductory information on time series forecasting and related machine learning methodologies. The novel contribution of the chapter is the exploration developed in the solutions and recommendations section while examining the effect of stationarity in the time series forecasting methodologies of machine learning with improved benchmark results.
dc.language.isoeng
dc.publisherIGI Global
dc.subjectGeneral Decision Sciences
dc.subjectDecision Sciences (miscellaneous)
dc.subjectStrategy and Management
dc.subjectGeneral Business, Management and Accounting
dc.subjectBusiness and International Management
dc.subjectMarketing
dc.subjectSocial Sciences & Humanities
dc.subjectSosyal ve Beşeri Bilimler
dc.subjectİşletme
dc.subjectİŞ
dc.subjectEkonomi ve İş
dc.subjectSosyal Bilimler (SOC)
dc.subjectÜretim Yönetimi
dc.subjectYÖNETİM
dc.titleDriving Innovation and Productivity Through Sustainable Automation
dc.typeKitapta Bölüm
dc.contributor.departmentİstanbul Üniversitesi , İşletme Fakültesi , İşletme Bölümü
dc.contributor.firstauthorID2529448


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