Wavelet Denoising Based Multivariate Polynomial For Anchovy Catches Forecasting

dc.contributor.author	Rodriguez, Nibaldo
dc.contributor.author	Cabrera, Guillermo
dc.date.accessioned	2009-09-17T19:27:08Z
dc.date.available	2009-09-17T19:27:08Z
dc.date.issued	2009-09-17T19:27:08Z
dc.identifier.isbn	978-987-24967-3-9
dc.identifier.uri	http://hdl.handle.net/10226/475
dc.description.abstract	In 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.	en
dc.language.iso	en	en
dc.relation.ispartofseries	Rodriguez, 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.subject	Forecasting	en
dc.subject	multivariate polynomial	en
dc.subject	wavelet transform	en
dc.title	Wavelet Denoising Based Multivariate Polynomial For Anchovy Catches Forecasting	en
dc.type	Article	en