Time series forecasting by evolving artificial neural networks using genetic algorithms and differential evolution

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dc.contributor.author Peralta, Juan
dc.contributor.author Li, Xiaodong
dc.contributor.author Gutiérrez Sánchez, Germán
dc.contributor.author Sanchis de Miguel, María Araceli
dc.date.accessioned 2010-12-30T12:40:48Z
dc.date.available 2010-12-30T12:40:48Z
dc.date.issued 2010
dc.identifier.bibliographicCitation 2010 IEEE World Congress on Computational Intelligence (WCCI 2010) / 2010 International Joint Conference on Neural Networks (IJCNN 2010). IEEE, 2010. pp.3999- 4006
dc.identifier.isbn 978-1-4244-6916-1
dc.identifier.issn 1098-7576
dc.identifier.uri http://hdl.handle.net/10016/9916
dc.description Proceeding of:IEEE World Congress on Computational Intelligence, (WCCI 2010) / 2010 International Joint Conference on Neural Networks (IJCNN 2010). July, 18-23, 2010. Barcelona, Spain.
dc.description.abstract Accurate time series forecasting are important for displaying the manner in which the past continues to affect the future and for planning our day to-day activities. In recent years, a large literature has evolved on the use of evolving artificial neural networks (EANNs) in many forecasting applications. Evolving neural networks are particularly appealing because of their ability to model an unspecified nonlinear relationship between time series variables. This paper evaluates two methods to evolve neural networks architectures, one carried out with genetic algorithm and a second one carry out with differential evolution algorithm. A comparative study between these two methods, with a set of referenced time series will be shown. The object of this study is to try to improve the final forecasting getting an accurate system.
dc.format.mimetype application/octet-stream
dc.format.mimetype application/octet-stream
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.rights © IEEE
dc.subject.other Differential evolution algorithm
dc.subject.other Evolving artificial neural networks
dc.subject.other Genetic algorithms
dc.subject.other Time series forecasting
dc.title Time series forecasting by evolving artificial neural networks using genetic algorithms and differential evolution
dc.type conferenceObject
dc.relation.publisherversion http://dx.doi.org/10.1109/IJCNN.2010.5596901
dc.subject.eciencia Informática
dc.identifier.doi 10.1109/IJCNN.2010.5596901
dc.rights.accessRights openAccess
dc.type.version publishedVersion
dc.relation.eventdate July, 18-23, 2010.
dc.relation.eventplace Barcelona (Spain)
dc.relation.eventtitle IEEE World Congress on Computational Intelligence, (WCCI 2010) / 2010 International Joint Conference on Neural Networks (IJCNN 2010)
dc.identifier.publicationfirstpage 3999
dc.identifier.publicationlastpage 4006
dc.identifier.publicationtitle 2010 IEEE World Congress on Computational Intelligence (WCCI 2010) / 2010 International Joint Conference on Neural Networks (IJCNN 2010)
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