Wavelet smoothing based multivariate polynomial for anchovy catches forecasting

Nibaldo Rodriguez; Eleuterio Yañez

doi:10.1109/CIS.2009.224

Wavelet smoothing based multivariate polynomial for anchovy catches forecasting

Nibaldo Rodriguez, Eleuterio Yañez

SCHOOL OF COMPUTER ENGINEERING

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	CIS 2009 - 2009 International Conference on Computational Intelligence and Security
Pages	38-41
Number of pages	4
DOIs	https://doi.org/10.1109/CIS.2009.224
State	Published - 2009
Event	2009 International Conference on Computational Intelligence and Security, CIS 2009 - Beijing, China Duration: 11 Dec 2009 → 14 Dec 2009

Publication series

Name	CIS 2009 - 2009 International Conference on Computational Intelligence and Security
Volume	1

Conference

Conference	2009 International Conference on Computational Intelligence and Security, CIS 2009
Country/Territory	China
City	Beijing
Period	11/12/09 → 14/12/09

Keywords

Forecasting
Wavelet analysis

Access to Document

10.1109/CIS.2009.224

Cite this

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title = "Wavelet smoothing based multivariate polynomial for anchovy catches forecasting",

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.",

keywords = "Forecasting, Wavelet analysis",

author = "Nibaldo Rodriguez and Eleuterio Ya{\~n}ez",

year = "2009",

doi = "10.1109/CIS.2009.224",

language = "English",

isbn = "9780769539317",

series = "CIS 2009 - 2009 International Conference on Computational Intelligence and Security",

pages = "38--41",

booktitle = "CIS 2009 - 2009 International Conference on Computational Intelligence and Security",

note = "2009 International Conference on Computational Intelligence and Security, CIS 2009 ; Conference date: 11-12-2009 Through 14-12-2009",

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Rodriguez, N & Yañez, E 2009, Wavelet smoothing based multivariate polynomial for anchovy catches forecasting. in CIS 2009 - 2009 International Conference on Computational Intelligence and Security., 5376743, CIS 2009 - 2009 International Conference on Computational Intelligence and Security, vol. 1, pp. 38-41, 2009 International Conference on Computational Intelligence and Security, CIS 2009, Beijing, China, 11/12/09. https://doi.org/10.1109/CIS.2009.224

Wavelet smoothing based multivariate polynomial for anchovy catches forecasting. / Rodriguez, Nibaldo; Yañez, Eleuterio.
CIS 2009 - 2009 International Conference on Computational Intelligence and Security. 2009. p. 38-41 5376743 (CIS 2009 - 2009 International Conference on Computational Intelligence and Security; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Yañez, Eleuterio

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N2 - 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.

AB - 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.

KW - Forecasting

KW - Wavelet analysis

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BT - CIS 2009 - 2009 International Conference on Computational Intelligence and Security

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Y2 - 11 December 2009 through 14 December 2009

ER -

Wavelet smoothing based multivariate polynomial for anchovy catches forecasting

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Keywords

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