dc.creator | MARIA DEL PILAR GOMEZ GIL | |
dc.creator | ANGEL MARIO GARCIA PEDRERO | |
dc.creator | JUAN MANUEL RAMIREZ CORTES | |
dc.date | 2010 | |
dc.date.accessioned | 2023-07-25T16:23:46Z | |
dc.date.available | 2023-07-25T16:23:46Z | |
dc.identifier | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1495 | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/7806690 | |
dc.description | Even 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.format | application/pdf | |
dc.language | eng | |
dc.publisher | Springer-Verlag Berlin Heidelberg | |
dc.relation | citation: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.rights | info:eu-repo/semantics/openAccess | |
dc.rights | http://creativecommons.org/licenses/by-nc-nd/4.0 | |
dc.subject | info:eu-repo/classification/cti/1 | |
dc.subject | info:eu-repo/classification/cti/22 | |
dc.subject | info:eu-repo/classification/cti/2203 | |
dc.subject | info:eu-repo/classification/cti/2203 | |
dc.title | Composite recurrent neural networks for long-term prediction of highly-dynamic time series supported by wavelet decomposition | |
dc.type | info:eu-repo/semantics/article | |
dc.type | info:eu-repo/semantics/acceptedVersion | |
dc.audience | students | |
dc.audience | researchers | |
dc.audience | generalPublic | |