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Simulating price interactions by mining multivariate financial time series

dc.contributor.authorSilva, Bruno
dc.contributor.authorCavique, Luís
dc.contributor.authorMarques, Nuno C.
dc.date.accessioned2015-05-05T08:51:07Z
dc.date.available2015-05-05T08:51:07Z
dc.date.issued2013
dc.descriptionConferência realizada em Beijing (China) de 3-9 de Agosto de 2013por
dc.description.abstractThis position paper proposes a framework based on a feature clustering method using Emergent Self-Organizing Maps over streaming data (UbiSOM) and Ramex-Forum – a sequence pattern mining model for financial time series modeling based on observed instantaneous and long term relations on market data. The proposed framework aims at producing realistic monte-carlo based simulations of an entire portfolio behavior over distinct market scenarios, obtained from models generated by these two approaches.por
dc.identifier.citationSilva, Bruno; Cavique, Luís; Marques, Nuno C. - Simulating price interactions by mining multivariate financial time series, In IJCAI 13. International Joint Conference on Artificial Intelligence, 23, Beijing, 2013 - "International ... [Em linha] : proceedings". [S.l.] : IJCAI : AAAI Press, 2013. 5 p.por
dc.identifier.urihttp://hdl.handle.net/10400.2/3919
dc.language.isoporpor
dc.peerreviewedyespor
dc.publisherInternational Joint Conference on Artificial Intelligence (IJCAI)/AAAI Presspor
dc.subjectSelf-organizing mapspor
dc.subjectPattern miningpor
dc.titleSimulating price interactions by mining multivariate financial time seriespor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceBeijing, Chinapor
oaire.citation.endPage5por
oaire.citation.startPage1por
oaire.citation.title23rd. International Joint Conference on Artificial Intelligence (IJCAI)por
person.familyNameCavique
person.givenNameLuís
person.identifier.ciencia-id911E-84AC-3956
person.identifier.orcid0000-0002-5590-1493
rcaap.rightsopenAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublication40906a16-46a2-42f1-b26d-7db7012294ee
relation.isAuthorOfPublication.latestForDiscovery40906a16-46a2-42f1-b26d-7db7012294ee

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