Time Series Prediction Evolving Voronoi Regions

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dc.contributor.author Luque, Cristóbal
dc.contributor.author Valls, José M.
dc.contributor.author Isasi, Pedro
dc.date.accessioned 2012-11-12T14:08:10Z
dc.date.accessioned 2012-11-12T15:23:58Z
dc.date.available 2012-11-12T15:23:58Z
dc.date.issued 2011-02
dc.identifier.bibliographicCitation Applied Intelligence. Vol. 34, Issue 1 (Feb. 2011), pp. 116-126
dc.identifier.issn 0924-669X (Print)
dc.identifier.issn 1573-7497 (Online)
dc.identifier.uri http://hdl.handle.net/10016/15865
dc.description.abstract Time series prediction is a complex problem that consists of forecasting the future behavior of a set of data with the only information of the previous data. The main problem is the fact that most of the time series that represent real phenomena include local behaviors that cannot be modelled by global approaches. This work presents a new procedure able to find predictable local behaviors, and thus, attaining a better level of total prediction. This new method is based on a division of the input space into Voronoi regions by means of Evolution Strategies. Our method has been tested using different time series domains. One of them that represents the water demand in a water tank, through a long period of time. The other two domains are well known examples of chaotic time series (Mackey-Glass) and natural phenomenon time series (Sunspot). Results prove that, in most of cases, the proposed algorithm obtain better results than other algorithms commonly used.
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.language.iso eng
dc.publisher Springer
dc.rights © Springer Science+Business Media, LLC
dc.subject.other Times series
dc.subject.other Artificial Intelligence
dc.subject.other Evolutive algorithms
dc.subject.other Evolution Strategies
dc.subject.other Machine Learning
dc.subject.other Voronoi Regions
dc.title Time Series Prediction Evolving Voronoi Regions
dc.type article
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1007/s10489-009-0184-9
dc.subject.eciencia Informática
dc.identifier.doi 10.1007/s10489-009-0184-9
dc.rights.accessRights openAccess
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 116
dc.identifier.publicationissue 1
dc.identifier.publicationlastpage 126
dc.identifier.publicationtitle Applied Intelligence
dc.identifier.publicationvolume 34
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