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

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dc.contributor.author Peralta, Juan
dc.contributor.author Gutiérrez Sánchez, Germán
dc.contributor.author Sanchis de Miguel, María Araceli
dc.date.accessioned 2011-01-11T09:46:48Z
dc.date.available 2011-01-11T09:46:48Z
dc.date.issued 2010
dc.identifier.bibliographicCitation Artificial neural networks : ICANN 2010, 20th International Conference, Proceedings, Part I. Springer, 2010 (Lecture Notes in Computer Science, vol. 6352), pp. 50-53.
dc.identifier.isbn 978-3-642-15818-6
dc.identifier.issn 0302-9743 (Print)
dc.identifier.issn 1611-3349 (Online)
dc.identifier.uri http://hdl.handle.net/10016/9933
dc.description Proceeding of: ICANN 2010, 20th International Conference, Thessaloniki, Greece, September 15-18, 2010
dc.description.abstract Accurate 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.sponsorship The research reported here has been supported by the Spanish Ministry of Science and Innovation under project TRA2007-67374-C02- 02.
dc.format.mimetype application/octet-stream
dc.format.mimetype application/octet-stream
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartofseries Lecture notes in computer science, vol. 6352
dc.rights Springer-Verlag Berlin Heidelberg
dc.subject.other Evolutionary computation
dc.subject.other Genetic algorithms
dc.subject.other Artificial Neural Networks
dc.subject.other Time Series
dc.subject.other Forecasting
dc.subject.other Ensembles
dc.title Time series forecasting by evolving artificial neural networks using “Shuffle”, cross-validation and ensembles
dc.type conferenceObject
dc.type bookPart
dc.relation.publisherversion
dc.subject.eciencia Informática
dc.identifier.doi 10.1007/978-3-642-15819-3_7
dc.rights.accessRights openAccess
dc.type.version acceptedVersion
dc.relation.eventdate September 15-18, 2010
dc.relation.eventplace Thessaloniki (Greece)
dc.relation.eventtitle ICANN 2010, 20th International Conference
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 50
dc.identifier.publicationlastpage 53
dc.identifier.publicationtitle Artificial neural networks : ICANN 2010
dc.identifier.publicationvolume 6352
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