Multi-step-ahead forecasting model for monthly anchovy catches based on wavelet analysis

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Resumen

This paper presents a p-step-ahead forecasting strategy based on two stages to improve pelagic fish-catch time-series modeling by considering annual and interannual fluctuations for northern Chile (18°S-24°S). In the first stage, the stationary wavelet transform is used to separate the raw time series into an annual component and an interannual component, whereas the periodicities of each component are obtained using the Morlet wavelet power spectrum. In the second stage, a linear autoregressive model is constructed to predict each component and the unknown p-next values are forecasted by the addition of the two predicted components. We demonstrate the utility of the proposed forecasting model on monthly anchovy-catches time series for periods from January 1963 to December 2007. Empirical results obtained for 10-month-ahead forecasting showed the effectiveness of the proposed wavelet autoregressive strategy.

Idioma originalInglés
Número de artículo798464
PublicaciónJournal of Applied Mathematics
Volumen2014
DOI
EstadoPublicada - 2014
Publicado de forma externa

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