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In this paper, the aim is to apply a functional autoregressive (FAR) model combined with multiscale wavelet analysis for monthly bigeye tuna catches forecasting in the ocean ecosystem of the equatorial Indian ocean. Wavelet technique performs a time-frequency analysis of a time series, which permits to decompose the raw time series into trend and residual components. In wavelet domain, the trend component...
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 paprer, a multivariate polynomial (MP) combined with smoothing techniques is proposed to forecast 1-month ahead monthly anchovy catches in the north area of Chile. The anchovy catches data is smoothed by using multiscale discrete stationary wavelet transform and then appropriate is used as inputs to the MP. The MP's parameters are estimated using the penalized least square method and the performance...
In this paper, multiscale wavelet analysis combined with a multivariate polynomial is presented to improve the accuracy and parsimony of 1-month ahead forecasting of monthly bigeye tuna catches in equatorial Indian Ocean. The proposed forecasting model is based on the decomposition the raw data set into trend and residuals components by using stationary wavelet transform. In wavelet domain, the trend...
In this paper, the aim is to apply a functional autoregressive (FAR) model combined with wavelet smoothing analysis for monthly bigeye tuna catches forecasting in the equatorial Indian ocean. The raw monthly tuna catches data is smoothed by using discrete stationary wavelet transform and then appropriate is used as inputs to the FAR model. We find that the proposed forecasting strategy achieves 97%...
In this paper, a forecasting strategy based on an additive autoregressive model combined with multiscale wavelet analysis to improve the accuracy of monthly tuna catches in equatorial Indian Ocean is proposed. The general idea of the proposed forecasting model is to decompose the raw tune data set into trend and residual components by using stationary wavelet transform. In wavelet domain, the trend...
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,...
The aim of this paper is to find a model to forecast 1-month ahead monthly sardines catches using a multivariate polynomial model combined with multi-scale stationary wavelet decomposition. The observed monthly sardines catches are decomposed into various sub-series employing wavelet decomposition and then appropriate sub-series are used as inputs to the autoregressive forecasting model. The forecasting...
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...
This paper deals with forecasting of monthly sardines catches in north area of Chile. The forecasting model is based on un-decimated stationary wavelet transform (SWT) combined with radial basis function (RBF) neural network and linear autoregressive (AR) model. The original monthly sardines catches data are decomposed into two sub-series employing 1-level SWT and the appropriate subseries are used...
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