dc.contributor.author | Kaut, Michal | |
dc.date.accessioned | 2022-05-09T07:18:23Z | |
dc.date.available | 2022-05-09T07:18:23Z | |
dc.date.created | 2021-09-24T10:28:15Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Computational Management Science. 2021, 18 411-429. | en_US |
dc.identifier.issn | 1619-697X | |
dc.identifier.uri | https://hdl.handle.net/11250/2994663 | |
dc.description.abstract | In this paper, we present and compare several methods for generating scenarios for stochastic-programming models by direct selection from historical data. The methods rangefromstandardsamplingandk-means,throughiterativesampling-basedselection methods, to a new moment-based optimization approach. We compare the models on a simple portfolio-optimization model and show how to use them in a situation when we are selecting whole sequences from the data, instead of single data points. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.subject | Scenario generation | en_US |
dc.subject | Stochastic programming | en_US |
dc.title | Scenario generation by selection from historical data | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © The Author 2021 | en_US |
dc.source.pagenumber | 411-429 | en_US |
dc.source.volume | 18 | en_US |
dc.source.journal | Computational Management Science | en_US |
dc.identifier.doi | 10.1007/s10287-021-00399-4 | |
dc.identifier.cristin | 1938061 | |
dc.relation.project | Norges forskningsråd: 268097 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |