Wavelet additive forecasting model to support the fisheries industry

Nibaldo Rodriguez, Wenceslao Palma, Eleuterio Yañez, Jose Miguel Rubio

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

1 Cita (Scopus)

Resumen

We present a forecasting strategy based on stationary wavelet decomposition combined with linear regression to improve the accuracy of one-month-ahead pelagic fish catches forecasting of the fisheries industry in southern zone of Chile. The general idea of the proposed forecasting model is to decompose the raw data set into long-term trend component and short-term fluctuation component by using wavelet decomposition. In wavelet domain, the components are predicted using a linear autoregressive model. Hence, proposed forecaster is the co-addition of two predicted components. We demonstrate the utility of the strategy on anchovy catches data set for monthly periods from 1978 to 2007. We find that the proposed forecasting scheme achieves a 98% of the explained variance with a reduced parsimonious.

Idioma originalInglés
Páginas (desde-hasta)3679-3682
Número de páginas4
PublicaciónAdvanced Science Letters
Volumen19
N.º12
DOI
EstadoPublicada - dic 2013
Publicado de forma externa

Huella

Profundice en los temas de investigación de 'Wavelet additive forecasting model to support the fisheries industry'. En conjunto forman una huella única.

Citar esto