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Wavelet smoothing based multivariate polynomial for anchovy catches forecasting

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Autor dc.contributor.author Rodriguez N.
Autor dc.contributor.author Yanez E.
Fecha Ingreso dc.date.accessioned 2014-04-05T00:18:43Z
Fecha Disponible dc.date.available 2014-04-05T00:18:43Z
Fecha en Repositorio dc.date.issued 2014-04-04
dc.identifier 10.1109/CIS.2009.224
dc.description.abstract In this paprer, a multivariate polynomial (MP) combined with smoothing techniques is proposed to forecast 1-month ahead monthly anchovy catches in the north area of Chile. The anchovy catches data is smoothed by using multiscale discrete stationary wavelet transform and then appropriate is used as inputs to the MP. The MP's parameters are estimated using the penalized least square method and the performance evaluation of the proposed forecaster showed that a 98 percent of the explained variance was captured with a reduced parsimony. © 2009 IEEE. en_US
dc.source CIS 2009 - 2009 International Conference on Computational Intelligence and Security
Link Descarga dc.source.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-77949310340&partnerID=40&md5=37d2660e202b6f9b2170069f1c0241b6
Title dc.title Wavelet smoothing based multivariate polynomial for anchovy catches forecasting en_US
Tipo dc.type Conference Paper
dc.description.keywords Discrete stationary wavelet transform; Multiscales; Multivariate polynomial; Penalized least-squares; Performance evaluation; Smoothing techniques; Artificial intelligence; Least squares approximations; Multivariable systems; Wavelet analysis; Wavelet transforms; Forecasting en_US


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