Multiscale legendre neural network for monthly anchovy catches forecasting

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

2 Scopus citations

Abstract

In this paper, a Legendre neural network (LNN) combined with multi-scale stationary wavelet decomposition is used to improve the prediction accuracy and parsimony of monthly anchovy catches forecasting in area north of Chile. The general idea behind this approach is to decompose the observed anchovy catches data into low frequency (LF) component and high frequency (HF) component using the multi-scale stationary wavelet transform to separately forecast each frequency component. In wavelet domain, the LF component and HF component are predicted with a linear autoregressive (AR) model and a LNN model; respectively. Hence, the proposed forecast is the co-addition of two predicted components. We find that the proposed forecasting method achieves 99% of the explained variance with reduced parsimony and high accuracy.

Original languageEnglish
Title of host publication3rd International Symposium on Intelligent Information Technology Application, IITA 2009
Pages598-601
Number of pages4
DOIs
StatePublished - 1 Dec 2009
Event3rd International Symposium on Intelligent Information Technology Application, IITA 2009 - NanChang, China
Duration: 21 Nov 200922 Nov 2009

Publication series

Name3rd International Symposium on Intelligent Information Technology Application, IITA 2009
Volume2

Conference

Conference3rd International Symposium on Intelligent Information Technology Application, IITA 2009
CountryChina
CityNanChang
Period21/11/0922/11/09

Keywords

  • Neural network
  • Wavelet analysis

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