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Wavelet polynomial autoregression for monthly bigeye tuna catches forecasting

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Autor dc.contributor.author Rodriguez N.
Fecha Ingreso dc.date.accessioned 2014-04-05T00:19:10Z
Fecha Disponible dc.date.available 2014-04-05T00:19:10Z
Fecha en Repositorio dc.date.issued 2014-04-04
dc.identifier 10.1109/ICECS.2009.87
dc.description.abstract In this paper, multiscale wavelet analysis combined with a multivariate polynomial is presented to improve the accuracy and parsimony of 1-month ahead forecasting of monthly bigeye tuna catches in equatorial Indian Ocean. The proposed forecasting model is based on the decomposition the raw data set into trend and residuals components by using stationary wavelet transform. In wavelet domain, the trend component and residuals components are predicted with a linear autoregressive model and a multi-scale polynomial autoregressive model; respectively. We find that the proposed forecasting method achieves 99% of the explained variance with reduced parsimony and high accuracy. © 2009 IEEE. en_US
dc.source 2nd International Conference on Environmental and Computer Science, ICECS 2009
Link Descarga dc.source.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-77949795422&partnerID=40&md5=c327218fdc6c9c3015f95771517385c5
Title dc.title Wavelet polynomial autoregression for monthly bigeye tuna catches forecasting en_US
Tipo dc.type Conference Paper
dc.description.keywords Auto regressive models; Autoregression; Data sets; Equatorial Indian Ocean; Forecasting methods; Forecasting models; Linear autoregressive model; Multi-scale wavelet analysis; Multiscales; Multivariate polynomial; Stationary wavelet transforms; Wavelet domain; Computer science; Forecasting; Multivariable systems; Oceanography; Polynomials; Regression analysis; Wavelet analysis; Wavelet decomposition en_US


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