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Wavelet additive forecasting model to support the fisheries industry

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Autor dc.contributor.author Rodriguez N.
Autor dc.contributor.author Palma W.
Autor dc.contributor.author Yanez E.
Autor dc.contributor.author Rubio J.-M.
Fecha Tésis dc.date 2013
Fecha Ingreso dc.date.accessioned 2014-04-04T17:22:14Z
Fecha Disponible dc.date.available 2014-04-04T17:22:14Z
Fecha en Repositorio dc.date.issued 2014-04-04
dc.description.abstract 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. © 2013 American Scientific Publishers. en_US
dc.source Advanced Science Letters
Link Descarga dc.source.uri http://dx.doi.org/10.1166/asl.2013.5192
Link Descarga dc.source.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-84878549270&partnerID=40&md5=88fd9ff1cdae20095cfe0ffaa6bb16d3
Title dc.title Wavelet additive forecasting model to support the fisheries industry en_US
dc.description.keywords Forecasting; Linear regression; Wavelet decomposition en_US


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