Actas de congresos
Multiscale RBF Neural Network for Forecasting of Monthly Hake Catches off Southern Chile
Fecha
2013Institución
Resumen
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.
Index Terms—Neural network, forecasting, nonlinear least
squares.