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In this paper, a multivariate polynomial (MP) model combined with wavelet analysis is proposed to improve the accuracy and parsimony of 1-month ahead forecasting of monthly anchovy catches in northern Chile. The proposed forecasting model is based on the decomposition the raw data set into low frequency (LF) and high frequency (HF) components by using stationary wavelet transform. In wavelet domain,...
In this paper, a multi-scale stationary wavelet decomposition technique combined with functional auto-regression is used to improve the prediction accuracy and parsimony of anchovy monthly catches forecasting in area north of Chile (18 21'S-24 S). The general idea behind this approach is to decompose the observed anchovy catches data into low frequency (LF) component and high frequency (HF) component...
In this paper, a nonlinear additive autoregressive model combined with multiscale stationary wavelet transform is used to improve the accuracy and parsimony of one-monthahead forecasting of monthly anchovy catches in northern Chile (18?? 21'S-24?? S). The general idea of the proposed forecasting model is to decompose the raw data set into trend and residual components by using SWT. In wavelet domain,...
In this paper, a Legendre neural network (LNN) combined with multi-scale stationary wavelet decomposition is used to improve the prediction accuracy and parsimony of monthly anchovy catches forecasting in area north of Chile. The general idea behind this approach is to decompose the observed anchovy catches data into low frequency (LF) component and high frequency (HF) component using the multi-scale...
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