Monthly tuna catches forecasting using multiscale additive autoregression

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Resumen

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 component and residual component are forecasted with a linear autoregressive model and a nonlinear additive autoregressive model; respectively. Hence, the proposed forecast is the co-addition of two predicted components. We find that the proposed forecasting strategy achieves 98% of the explained variance with reduced parsimony and high accuracy.

Idioma originalInglés
Título de la publicación alojadaPACIIA 2009 - 2009 2nd Asia-Pacific Conference on Computational Intelligence and Industrial Applications
Páginas385-388
Número de páginas4
DOI
EstadoPublicada - 2009
Publicado de forma externa
Evento2009 2nd Asia-Pacific Conference on Computational Intelligence and Industrial Applications, PACIIA 2009 - Wuhan, China
Duración: 28 nov. 200929 nov. 2009

Serie de la publicación

NombrePACIIA 2009 - 2009 2nd Asia-Pacific Conference on Computational Intelligence and Industrial Applications
Volumen1

Conferencia

Conferencia2009 2nd Asia-Pacific Conference on Computational Intelligence and Industrial Applications, PACIIA 2009
País/TerritorioChina
CiudadWuhan
Período28/11/0929/11/09

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