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dc.contributor.authorFyfe, Colin
dc.contributor.authorHan, Ying
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.date.accessioned2017-09-05T11:02:23Z
dc.date.available2017-09-05T11:02:23Z
dc.date.issued2004
dc.identifier.citationNeurocomputing. Volumen 57, pp. 37-47. Elsevier BV.
dc.identifier.issn0925-2312 (Print)
dc.identifier.urihttp://hdl.handle.net/10366/134457
dc.description.abstractWe extend the principal curves algorithm by creating twinned principal curves which extend through two related data sets simultaneously. The criteria for accepting a pair of data points as neighbours for any other pair of data points is that each of the relevant points must be close in the appropriate space. We illustrate the algorithm's predictive power on artificial data sets before using it to predict on a real financial time series. We compare the error from this twinning with that achieved by a related algorithm which twins self-organising maps.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherElsevier BV
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleForecasting using twinned principal curves and twinned self-organising maps
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported