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dc.contributor.authorYahşi, Mustafa
dc.contributor.authorÇanakoğlu, Ethem
dc.contributor.authorAğralı, Semra
dc.date.accessioned2019-05-20T08:08:55Z
dc.date.available2019-05-20T08:08:55Z
dc.date.issued2019en_US
dc.identifier.citationYahsi, M., Canakoglu, E., & Agrali, S. (February, 2019) Carbon price forecasting models based on big data analytics, Carbon Management, 10:2, 175-187, DOI: 10.1080/17583004.2019.1568138en_US
dc.identifier.issn1758-3012
dc.identifier.issn1758-3004
dc.identifier.urihttps://doi.org/10.1080/17583004.2019.1568138
dc.identifier.urihttps://hdl.handle.net/20.500.11779/1084
dc.description.abstractAfter the establishment of the European Union's Emissions Trading System (EU-ETS) carbon pricing attracted many researchers. This paper aims to develop a prediction model that anticipates future carbon prices given a real-world data set. We treat the carbon pricing issue as part of big data analytics to achieve this goal. We apply three fundamental methodologies to characterize the carbon price. First method is the artificial neural network, which mimics the principle of human brain to process relevant data. As a second approach, we apply the decision tree algorithm. This algorithm is structured through making multiple binary decisions, and it is mostly used for classification. We employ two different decision tree algorithms, namely traditional and conditional, to determine the type of decision tree that gives better results in terms of prediction. Finally, we exploit the random forest, which is a more complex algorithm compared to the decision tree. Similar to the decision tree, we test both traditional and conditional random forest algorithms to analyze their performances. We use Brent crude futures, coal, electricity and natural gas prices, and DAX and S&P Clean Energy Index as explanatory variables. We analyze the variables' effects on carbon price forecasting. According to our results, S&P Clean Energy Index is the most influential variable in explaining the changes in carbon price, followed by DAX Index and coal price. Moreover, we conclude that the traditional random forest is the best algorithm based on all indicators. We provide the details of these methods and their comparisons.en_US
dc.language.isoengen_US
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofCarbon Managementen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networken_US
dc.subjectBig dataen_US
dc.subjectCarbon priceen_US
dc.subjectDecision treeen_US
dc.subjectForecastsen_US
dc.subjectRandom foresten_US
dc.titleCarbon price forecasting models based on big data analyticsen_US
dc.typearticleen_US
dc.departmentMühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.identifier.volume10en_US
dc.identifier.issue2en_US
dc.identifier.startpage175en_US
dc.identifier.endpage187en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.description.wosidWOS:000468369600006en_US
dc.description.scopusid2-s2.0-85065429258en_US
dc.contributor.institutionauthorAğralı, Semra
dc.description.woscitationindexScience Citation Index Expanded - Social Sciences Citation Indexen_US
dc.identifier.wosqualityQ3en_US
dc.description.WoSDocumentTypeArticleen_US
dc.description.WoSInternationalCollaborationUluslararası işbirliği ile yapılmayan - HAYIRen_US
dc.description.WoSPublishedMonthMarten_US
dc.description.WoSIndexDate2019en_US
dc.description.WoSYOKperiodYÖK - 2018-19en_US
dc.identifier.doi10.1080/17583004.2019.1568138en_US
dc.identifier.scopusqualityQ1en_US


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