Monthly bigeye tuna catches forecasting usingwavelet functional autoregression

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Resumen

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 and residual component are forecasted with a linear autoregressive model and a FAR 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 alojada3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
Páginas67-70
Número de páginas4
DOI
EstadoPublicada - 2010
Publicado de forma externa
Evento3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010 - Phuket, Tailandia
Duración: 9 ene 201010 ene 2010

Serie de la publicación

Nombre3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010

Conferencia

Conferencia3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
País/TerritorioTailandia
CiudadPhuket
Período9/01/1010/01/10

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