Título
Composite recurrent neural networks for long-term prediction of highly-dynamic time series supported by wavelet decomposition
Autor
MARIA DEL PILAR GOMEZ GIL
ANGEL MARIO GARCIA PEDRERO
JUAN MANUEL RAMIREZ CORTES
Nivel de Acceso
Acceso Abierto
Materias
Resumen o descripción
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.
Editor
Springer-Verlag Berlin Heidelberg
Fecha de publicación
2010
Tipo de publicación
Artículo
Versión de la publicación
Versión aceptada
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Estudiantes
Investigadores
Público en general
Sugerencia de citación
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
Repositorio Orígen
Repositorio Institucional del INAOE
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539