| 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 |