Monthly tuna catches forecasting using multiscale additive autoregression

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

Abstract

In this paper, a forecasting strategy based on an additive autoregressive model combined with multiscale wavelet analysis to improve the accuracy of monthly tuna catches in equatorial Indian Ocean is proposed. The general idea of the proposed forecasting model is to decompose the raw tune data set into trend and residual components by using stationary wavelet transform. In wavelet domain, the trend component and residual component are forecasted with a linear autoregressive model and a nonlinear additive autoregressive model; respectively. Hence, the proposed forecast is the co-addition of two predicted components. We find that the proposed forecasting strategy achieves 98% of the explained variance with reduced parsimony and high accuracy.

Original languageEnglish
Title of host publicationPACIIA 2009 - 2009 2nd Asia-Pacific Conference on Computational Intelligence and Industrial Applications
Pages385-388
Number of pages4
DOIs
StatePublished - 1 Dec 2009
Event2009 2nd Asia-Pacific Conference on Computational Intelligence and Industrial Applications, PACIIA 2009 - Wuhan, China
Duration: 28 Nov 200929 Nov 2009

Publication series

NamePACIIA 2009 - 2009 2nd Asia-Pacific Conference on Computational Intelligence and Industrial Applications
Volume1

Conference

Conference2009 2nd Asia-Pacific Conference on Computational Intelligence and Industrial Applications, PACIIA 2009
CountryChina
CityWuhan
Period28/11/0929/11/09

Keywords

  • Forecasting
  • Regression
  • Wavelet abalysis

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