Wavelet Denoising Based Multivariate Polynomial For Anchovy Catches Forecasting

dc.creatorRodriguez, Nibaldo
dc.creatorCabrera, Guillermo
dc.date.accessioned2009-09-17T19:27:08Z
dc.date.accessioned2019-05-28T15:16:22Z
dc.date.available2009-09-17T19:27:08Z
dc.date.available2019-05-28T15:16:22Z
dc.date.created2009-09-17T19:27:08Z
dc.date.issued2009-09-17T19:27:08Z
dc.identifier978-987-24967-3-9
dc.identifierhttp://hdl.handle.net/10226/475
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/2877335
dc.description.abstractIn this paprer, a multivariate polynomial (MP) combined with denoising techniques is proposed to forecast 1-month ahead monthly anchovy catches in the north area of Chile. The anchovy catches data is denoised by using 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 (LS) method and the performance evaluation of the proposed forecaster showed that a 98% of the explained variance was captured with a reduced parsimony.
dc.languageen
dc.relationRodriguez, N. y Cabrera, G., (2009, julio). Wavelet Denoising Based Multivariate Polynomial For Anchovy Catches Forecasting. Trabajo presentado en el Congreso de Inteligencia Computacional Aplicada (CICA), realizado en Buenos Aires del 23 al 24 de julio de 2009.
dc.subjectForecasting
dc.subjectmultivariate polynomial
dc.subjectwavelet transform
dc.titleWavelet Denoising Based Multivariate Polynomial For Anchovy Catches Forecasting
dc.typeArtículos de revistas


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