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Multidimensional Prediction Models When the Resolution Context Changes

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Multidimensional Prediction Models When the Resolution Context Changes

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dc.contributor.author Martínez Usó, Adolfo es_ES
dc.contributor.author Hernández Orallo, José es_ES
dc.date.accessioned 2016-06-09T10:52:23Z
dc.date.available 2016-06-09T10:52:23Z
dc.date.issued 2015-09-07
dc.identifier.isbn 978-3-319-23524-0
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10251/65588
dc.description The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-23525-7_31 es_ES
dc.description.abstract Multidimensional data is systematically analysed at multiple granularities by applying aggregate and disaggregate operators (e.g., by the use of OLAP tools). For instance, in a supermarket we may want to predict sales of tomatoes for next week, but we may also be interested in predicting sales for all vegetables (higher up in the product hierarchy) for next Friday (lower down in the time dimension). While the domain and data are the same, the operating context is different. We explore several approaches for multidimensional data when predictions have to be made at different levels (or contexts) of aggregation. One method relies on the same resolution, another approach aggregates predictions bottom-up, a third approach disaggregates predictions top-down and a final technique corrects predictions using the relation between levels. We show how these strategies behave when the resolution context changes, using several machine learning techniques in four application domains. es_ES
dc.description.sponsorship This work was supported by the Spanish MINECO under grants TIN 2010-21062-C02-02 and TIN 2013-45732-C4-1-P, and the REFRAME project, granted by the European Coordinated Research on Longterm Challenges in Information and Communication Sciences Technologies ERA-Net (CHIST-ERA), and funded by MINECO in Spain (PCIN-2013-037) and by Generalitat Valenciana PROMETEOII2015/013. es_ES
dc.format.extent 16 es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation MINECO/TIN 2010-21062-C02-02 es_ES
dc.relation MINECO/TIN 2013-45732-C4-1-P es_ES
dc.relation MINECO/PCIN-2013-037 es_ES
dc.relation GV/PROMETEOII/2015/013 es_ES
dc.relation.ispartof Machine Learning and Knowledge Discovery in Databases es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science;9285
dc.rights Reserva de todos los derechos es_ES
dc.subject Multidimensional data es_ES
dc.subject Operating context aggregation es_ES
dc.subject Disaggregation es_ES
dc.subject OLAP cubes es_ES
dc.subject Quantification es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Multidimensional Prediction Models When the Resolution Context Changes es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.1007/978-3-319-23525-7_31
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Martínez Usó, A.; Hernández Orallo, J. (2015). Multidimensional Prediction Models When the Resolution Context Changes. En Machine Learning and Knowledge Discovery in Databases. Springer. 509-524. doi:10.1007/978-3-319-23525-7_31 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.conferencename European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2015) es_ES
dc.relation.conferencedate September 7-11, 2015 es_ES
dc.relation.conferenceplace Porto, Portugal es_ES
dc.relation.publisherversion http://link.springer.com/chapter/10.1007/978-3-319-23525-7_31 es_ES
dc.description.upvformatpinicio 509 es_ES
dc.description.upvformatpfin 524 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.senia 302743 es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Generalitat Valenciana es_ES
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