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Multivariate Scenario Generation －An Arima and Copula Approach

dc.contributor.authorMishra, Sambeet
dc.contributor.authorBordin, Chiara
dc.contributor.authorWürsig, Christoph
dc.contributor.authorPalu, Ivo
dc.date.accessioned2019-09-02T07:52:35Z
dc.date.available2019-09-02T07:52:35Z
dc.date.created2019-08-12T16:31:57Z
dc.date.issued2019
dc.identifier.citationInternational Journal of Modeling and Optimization. 2019, 9 (3), 146-149.nb_NO
dc.identifier.issn2010-3697
dc.identifier.urihttp://hdl.handle.net/11250/2611941
dc.description.abstractIn mathematical optimization uncertainty is expressed through scenarios. auto-regressive integrated moving average (ARIMA) is one of the known practice to generate scenarios. This paper is about scenario generation using multivariate data: electrical power demand, wind power generation and energy market price. An ARIMA model along with Copula is implemented for scenario generation. The results are presented and discussed.nb_NO
dc.description.abstractMultivariate Scenario Generation －An Arima and Copula Approachnb_NO
dc.language.isoengnb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleMultivariate Scenario Generation －An Arima and Copula Approachnb_NO
dc.typeJournal articlenb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber146-149nb_NO
dc.source.volume9nb_NO
dc.source.journalInternational Journal of Modeling and Optimizationnb_NO
dc.source.issue3nb_NO
dc.identifier.doi10.7763/IJMO.2019.V9.700
dc.identifier.cristin1715374
cristin.unitcode7548,50,0,0
cristin.unitnameEnergisystemer
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode0


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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