Data Partition And Variable Selection For Time Series Prediction Using Wrappers

dc.creatorPuma-Villanueva W.J.
dc.creatorDos Santos E.P.
dc.creatorVon Zuben F.J.
dc.date2006
dc.date2015-06-30T18:15:30Z
dc.date2015-11-26T14:28:20Z
dc.date2015-06-30T18:15:30Z
dc.date2015-11-26T14:28:20Z
dc.date.accessioned2018-03-28T21:31:31Z
dc.date.available2018-03-28T21:31:31Z
dc.identifier0780394909; 9780780394902
dc.identifierIeee International Conference On Neural Networks - Conference Proceedings. , v. , n. , p. 4740 - 4747, 2006.
dc.identifier10987576
dc.identifier
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-40649114243&partnerID=40&md5=952562afaf2904ddba5f4e5d444ab3d3
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/103709
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/103709
dc.identifier2-s2.0-40649114243
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1246564
dc.descriptionThe purpose of this paper is a comparative study of a non-exhaustive, though representative, set of methodologies already available for the partition of the training dataset in time series prediction, and also for variable selection under the wrapper paradigm. The partition policy of the training dataset and the choice of a proper set of variables for the regression vector are known to have a significant influence in the accuracy of the predictor, no matter the choice of the prediction model. However, there has been no extensive search for a figure of merit supporting a comparative analysis. Here, two partition policies, denoted sequential and random, are compared, and among the variable selection approaches using wrappers, forward selection is contrasted with sensitivity based pruning. Five real financial time series with trends and seasonality have been considered and multilayer perceptrons are adopted as the predictor. The obtained results indicate with high confidence that the rarely adopted random partition and the computationally intensive forward selection overcomes the contestants in the whole set of experiments. © 2006 IEEE.
dc.description
dc.description
dc.description4740
dc.description4747
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dc.languageen
dc.publisher
dc.relationIEEE International Conference on Neural Networks - Conference Proceedings
dc.rightsfechado
dc.sourceScopus
dc.titleData Partition And Variable Selection For Time Series Prediction Using Wrappers
dc.typeActas de congresos


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