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http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1495| Composite recurrent neural networks for long-term prediction of highly-dynamic time series supported by wavelet decomposition | |
| MARIA DEL PILAR GOMEZ GIL ANGEL MARIO GARCIA PEDRERO JUAN MANUEL RAMIREZ CORTES | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| 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. | |
| Springer-Verlag Berlin Heidelberg | |
| 2010 | |
| Artículo | |
| Inglés | |
| Estudiantes Investigadores Público en general | |
| 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 | |
| ELECTRÓNICA | |
| Versión aceptada | |
| acceptedVersion - Versión aceptada | |
| Aparece en las colecciones: | Artículos de Electrónica |
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