dc.creatorMARIA DEL PILAR GOMEZ GIL
dc.creatorANGEL MARIO GARCIA PEDRERO
dc.creatorJUAN MANUEL RAMIREZ CORTES
dc.date2010
dc.date.accessioned2023-07-25T16:23:46Z
dc.date.available2023-07-25T16:23:46Z
dc.identifierhttp://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1495
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7806690
dc.descriptionEven though it is known that chaotic time series cannot be accurately predicted, there is a need to forecast their behavior in may decision processes. Therefore several non-linear prediction strategies have been developed, many of them based on soft computing. In this chapter we present a new neural network architecutre, called Hybrid and based-on-Wavelet-Reconstructions Network (HWRN), which is able to perform recursive long-term prediction over highly dynamic and chaotic time series. HWRN is based on recurrent neural networks embedded in a two-layer neural structure, using as a learning aid, signals generated by wavelets coefficients obtained from the training time series. In the results reported here, HWRN was able to predict better than a feed-forward neural network and that a fully-connected, recurrent neural network with similar number of nodes. Using the benchmark known as NN5, which contains chaotic time series, HWRN obtained in average a SMAPE = 26% compared to a SMAPE = 61% obtained by a fully-connected recurrent neural network and a SMAPE = 49% obtained by a feed forward network.
dc.formatapplication/pdf
dc.languageeng
dc.publisherSpringer-Verlag Berlin Heidelberg
dc.relationcitation:Gomez-Gil, P., et al., (2010). Composite recurrent neural networks for long-term prediction of highly-dynamic time series supported by wavelet decomposition, O. Castillo et al. (Eds.): Soft Computing for Intell. Control and Mob. Robot., SCI (318): 253–268
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectinfo:eu-repo/classification/cti/1
dc.subjectinfo:eu-repo/classification/cti/22
dc.subjectinfo:eu-repo/classification/cti/2203
dc.subjectinfo:eu-repo/classification/cti/2203
dc.titleComposite recurrent neural networks for long-term prediction of highly-dynamic time series supported by wavelet decomposition
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.audiencestudents
dc.audienceresearchers
dc.audiencegeneralPublic


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