Hybrid wavelet-RBFNN model for monthly anchovy catches forecasting

Nibaldo Rodriguez, Broderick Crawford, Carlos Castro, Eleuterio Yañez

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

A hybrid method to forecast 1-month ahead monthly anchovy catches in the north area of Chile is proposed in this paper. This method combined two techniques, stationary wavelet transform (SWT) and radial basis function neural network (RBFNN). The observed monthly anchovy catches data is decomposed into two subseries using 1-level SWT and the appropriate subseries are used as inputs to the RBFNN to forecast original anchovy catches time series. The RBFNN architecture is composed of linear and nonlinear weights, which are estimates using the least square method and Levenberg-Marquardt algorithm; respectively. Wavelet-RBFNN based forecasting performance was evaluated by comparing it with classical RBFNN model. The benchmark results shown that a 99% of the explained variance was captured with a reduced parsimony and high speed convergence.

Idioma originalInglés
Título de la publicación alojadaArtificial Intelligence
Subtítulo de la publicación alojadaMethodology, Systems, and Applications - 13th International Conference, AIMSA 2008, Proceedings
Páginas345-352
Número de páginas8
DOI
EstadoPublicada - 2008
Evento13th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2008 - Varna, Bulgaria
Duración: 4 sep. 20086 sep. 2008

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen5253 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia13th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2008
País/TerritorioBulgaria
CiudadVarna
Período4/09/086/09/08

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