Wavelet polynomial autoregression for monthly bigeye tuna catches forecasting

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Resumen

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 component and residuals components are predicted with a linear autoregressive model and a multi-scale polynomial autoregressive model; respectively. We find that the proposed forecasting method achieves 99% of the explained variance with reduced parsimony and high accuracy.

Idioma originalInglés
Título de la publicación alojada2nd International Conference on Environmental and Computer Science, ICECS 2009
Páginas175-178
Número de páginas4
DOI
EstadoPublicada - 2009
Evento2nd International Conference on Environmental and Computer Science, ICECS 2009 - Dubai, Emiratos Árabes Unidos
Duración: 28 dic. 200930 dic. 2009

Serie de la publicación

Nombre2nd International Conference on Environmental and Computer Science, ICECS 2009

Conferencia

Conferencia2nd International Conference on Environmental and Computer Science, ICECS 2009
País/TerritorioEmiratos Árabes Unidos
CiudadDubai
Período28/12/0930/12/09

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