Publication:
Time series forecasting by evolving artificial neural networks using “Shuffle”, cross-validation and ensembles

Simple item page
dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Control, Aprendizaje y Optimización de Sistemas (CAOS)es
dc.contributor.authorPeralta, Juan
dc.contributor.authorGutiérrez Sánchez, Germán
dc.contributor.authorSanchis de Miguel, María Araceli
dc.date.accessioned2011-01-11T09:46:48Z
dc.date.available2011-01-11T09:46:48Z
dc.date.issued2010
dc.descriptionProceeding of: ICANN 2010, 20th International Conference, Thessaloniki, Greece, September 15-18, 2010
dc.description.abstractAccurate time series forecasting are important for several business, research, and application of engineering systems. Evolutionary Neural Networks are particularly appealing because of their ability to design, in an automatic way, a model (an Artificial Neural Network) for an unspecified nonlinear relationship for time series values. This paper evaluates two methods to obtain the pattern sets that will be used by the artificial neural network in the evolutionary process, one called ”shuffle” and another one carried out with cross-validation and ensembles. A study using these two methods will be shown with the aim to evaluate the effect of both methods in the accurateness of the final forecasting.
dc.description.sponsorshipThe research reported here has been supported by the Spanish Ministry of Science and Innovation under project TRA2007-67374-C02- 02.
dc.format.mimetypeapplication/octet-stream
dc.format.mimetypeapplication/octet-stream
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationArtificial neural networks : ICANN 2010, 20th International Conference, Proceedings, Part I. Springer, 2010 (Lecture Notes in Computer Science, vol. 6352), pp. 50-53.
dc.identifier.doi10.1007/978-3-642-15819-3_7
dc.identifier.isbn978-3-642-15818-6
dc.identifier.issn0302-9743 (Print)
dc.identifier.issn1611-3349 (Online)
dc.identifier.publicationfirstpage50
dc.identifier.publicationlastpage53
dc.identifier.publicationtitleArtificial neural networks : ICANN 2010
dc.identifier.publicationvolume6352
dc.identifier.urihttps://hdl.handle.net/10016/9933
dc.language.isoeng
dc.publisherSpringer
dc.relation.eventdateSeptember 15-18, 2010
dc.relation.eventplaceThessaloniki (Greece)
dc.relation.eventtitleICANN 2010, 20th International Conference
dc.relation.ispartofseriesLecture notes in computer science, vol. 6352
dc.relation.publisherversion
dc.rightsSpringer-Verlag Berlin Heidelberg
dc.rights.accessRightsopen access
dc.subject.ecienciaInformática
dc.subject.otherEvolutionary computation
dc.subject.otherGenetic algorithms
dc.subject.otherArtificial Neural Networks
dc.subject.otherTime Series
dc.subject.otherForecasting
dc.subject.otherEnsembles
dc.titleTime series forecasting by evolving artificial neural networks using “Shuffle”, cross-validation and ensembles
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
time_gutierrez_LNCS_2010_ps.pdf
Size:
117.15 KB
Format:
Adobe Portable Document Format
Download
Collections
DI - CAOS - Comunicaciones en Congresos y otros eventos
DI - CAOS - Capítulos de Monografías