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Wavelet Additive Forecasting Model to Support the Fisheries Industry

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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.
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Document Type: Research Article

Publication date: December 1, 2013

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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