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—We present a forecasting strategy based onstationary wavelet transform combined with radial basis function(RBF) neural network to improve the accuracy of 3-month-aheadhake catches forecasting of the sheries industry in the centralsouthern Chile. The general idea of the proposed forecastingmodel is to decompose the raw data set into an annualcycle component and an inter-annual component by using3-levels stationary wavelet decomposition. The components areindependently predicted using an autoregressive RBF neuralnetwork model. The RBF neural network model is composedof linear and nonlinear weights, which are estimates usingthe separable nonlinear least squares method. Consequently,the proposed forecaster is the co-addition of two predictedcomponents. We demonstrate the utility of the proposed modelon hake catches data set for monthly periods from 1963 to2008. Experimental results on hake catches data show thatthe autoregressive RBF neural network model is effective for3-month-ahead forecasting.Index Terms—Neural network, forecasting, nonlinear leastsquares.

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