RBF network combined with wavelet denoising for sardine catches forecasting

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

Abstract

This paper deals with time series of monthly sardines catches in the north area of Chile. The proposed method combines radial basis function neural network (RBFNN) with wavelet denoising algorithm. Wavelet dcnoising is based on stationary wavelet transform with hard thresholding rule and the RBFNN architecture is composed of linear and nonlinear weights, which are estimated by using the separable nonlinear least square method. The performance evaluation of the proposed forecasting model showed that a 93% of the explained variance was captured with a reduced parsimony.

Original languageEnglish
Title of host publicationICSOFT 2008 - 3rd International Conference on Software and Data Technologies, Proceedings
Pages308-311
Number of pages4
EditionABF/-
StatePublished - 1 Dec 2008
Event3rd International Conference on Software and Data Technologies, ICSOFT 2008 - Porto, Portugal
Duration: 5 Jul 20088 Jul 2008

Publication series

NameICSOFT 2008 - Proceedings of the 3rd International Conference on Software and Data Technologies
NumberABF/-
VolumeISDM

Conference

Conference3rd International Conference on Software and Data Technologies, ICSOFT 2008
CountryPortugal
CityPorto
Period5/07/088/07/08

Keywords

  • Forecasting
  • Neural networks
  • Wavelet denoising

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