Rodyti trumpą aprašą

dc.contributor.authorFouladgar, Mohammad Majid
dc.contributor.authorYazdani, Morteza
dc.contributor.authorKhazaee, Saeed
dc.contributor.authorZavadskas, Edmundas Kazimieras
dc.contributor.authorFouladgar, Vahid
dc.date.accessioned2023-09-18T20:00:10Z
dc.date.available2023-09-18T20:00:10Z
dc.date.issued2013
dc.identifier.issn0424-267X
dc.identifier.other(BIS)VGT02-000027717
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/145757
dc.description.abstractForecasting the changes of oil prices is of critical importance for authorities and plays a significant role in the dynamic global economy. This paper employs two prediction tools, including econometric and artificial neural network (ANN) models, for forecasting the price of WTI oil to conduct a comparative study. Forecasts from vector time series (vector autoregressive (VAR) and vector error correction (VEC) models) as econometric models are compared with those from ANN model based. For developing the models, 144 monthly data (2000/1-2011/12) comprising monthly oil price, production, reserves, fright rate, world GDP and inflation is applied. To obtain the best model for forecasting the oil price, various models comprising different combinations of training and testing dataset are tested. For achieving the aim, the most appropriate network structure and model is determined based on prediction accuracy and performance. The performance indexes for evaluating the VAR and ANN models contain of RMSE (Root Mean Square error), MAE (mean absolute error), and coefficient of determination (R2) criteria, indicate that ANN yields better results.eng
dc.format.extentp. 19-35
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbySocial Sciences Citation Index (Web of Science)
dc.relation.isreferencedbyJournal Citation Reports/Science Edition
dc.relation.isreferencedbySocial SciSearch
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.source.urihttp://www.ecocyb.ase.ro/nr42013pdf/Fouladgar%20M.,%20Zavadskas%20Ed%28T%29.pdf
dc.titleComparison of vector time series and ANN techniques for forecasting of WTI oil price
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsIDS Number: 284QD
dcterms.references53
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionAmirkabir University of Technology, Tehran, Iran
dc.contributor.institutionIslamic Azad University, Shahrekord, Iran
dc.contributor.institutionTehran University, Tehran, Iran
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.institutionIslamic Banking, Tehran University, Tehran, Iran
dc.contributor.facultyVilniaus Gedimino technikos universitetas / Vilniaus Gedimino technikos universitetas
dc.subject.researchfieldS 003 - Vadyba / Management
dc.subject.researchfieldS 004 - Ekonomika / Economics
dc.subject.enOil price
dc.subject.enANN
dc.subject.enVAR
dc.subject.enVEC
dc.subject.enWTI
dcterms.sourcetitleJournal of economic computation and economic cybernetics studies and research (ECECSR)
dc.description.issueno.4
dc.description.volumeVol. 47
dc.publisher.nameAcademy of Economic Studies
dc.publisher.cityBucharest
dc.identifier.doi000329334100002
dc.identifier.elaba4057879


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