Hybrid wavelet-RBFNN model for monthly anchovy catches forecasting

NIBALDO RODRIGUEZ AGURTO, BRODERICK CRAWFORD LABRIN, Carlos Castro, Eleuterio Yañez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A hybrid method to forecast 1-month ahead monthly anchovy catches in the north area of Chile is proposed in this paper. This method combined two techniques, stationary wavelet transform (SWT) and radial basis function neural network (RBFNN). The observed monthly anchovy catches data is decomposed into two subseries using 1-level SWT and the appropriate subseries are used as inputs to the RBFNN to forecast original anchovy catches time series. The RBFNN architecture is composed of linear and nonlinear weights, which are estimates using the least square method and Levenberg-Marquardt algorithm; respectively. Wavelet-RBFNN based forecasting performance was evaluated by comparing it with classical RBFNN model. The benchmark results shown that a 99% of the explained variance was captured with a reduced parsimony and high speed convergence.

Original languageEnglish
Title of host publicationArtificial Intelligence
Subtitle of host publicationMethodology, Systems, and Applications - 13th International Conference, AIMSA 2008, Proceedings
Pages345-352
Number of pages8
DOIs
StatePublished - 25 Sep 2008
Event13th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2008 - Varna, Bulgaria
Duration: 4 Sep 20086 Sep 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5253 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2008
CountryBulgaria
CityVarna
Period4/09/086/09/08

Keywords

  • Forecasting
  • Neural network
  • Wavelet transform

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