Multiscale RBF Neural Network for Forecasting of Monthly Hake Catches off Southern Chile

 

Nibaldo Rodriguez¹, Lida Barba² and Jose Miguel Rubio L.³

 

¹ School of Computer Engineering at the Pontificia Universidad Catolica de Valparaiso, Av. Brasil 2241, Chile (e-mail: nibaldo.rodriguez@ucv.cl).

² School of Computer Engineering at the Universidad Nacional de Chimborazo, Av. Antonio Jose de Sucre, Km 1.5, Ecuador.

³ School of Computer Engineering at the Pontificia Universidad Catolica de Valparaiso, Av. Brasil 2241, Chile.

 

Manuscript received on August 2, 2013.
Accepted for publication on September 30, 2013.

 

Abstract

We present a forecasting strategy based on stationary wavelet transform combined with radial basis function (RBF) neural network to improve the accuracy of 3-month-ahead hake catches forecasting of the fisheries industry in the central southern Chile. The general idea of the proposed forecasting model is to decompose the raw data set into an annual cycle component and an inter-annual component by using 3-levels stationary wavelet decomposition. The components are independently predicted using an autoregressive RBF neural network model. The RBF neural network model is composed of linear and nonlinear weights, which are estimates using the separable nonlinear least squares method. Consequently, the proposed forecaster is the co-addition of two predicted components. We demonstrate the utility of the proposed model on hake catches data set for monthly periods from 1963 to 2008. Experimental results on hake catches data show that the autoregressive RBF neural network model is effective for 3-month-ahead forecasting.

Key words: Neural network, forecasting, nonlinear least squares.

  

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Acknowledgment

This research was partially supported by the Chilean National Science Fund through the project Fondecyt-Regular 1131105 and by the VRIEA of the Pontificia Universidad Católica de Valparaíso.

 

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