Wavelet polynomial autoregression for monthly bigeye tuna catches forecasting

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

Abstract

In this paper, multiscale wavelet analysis combined with a multivariate polynomial is presented to improve the accuracy and parsimony of 1-month ahead forecasting of monthly bigeye tuna catches in equatorial Indian Ocean. The proposed forecasting model is based on the decomposition the raw data set into trend and residuals components by using stationary wavelet transform. In wavelet domain, the trend component and residuals components are predicted with a linear autoregressive model and a multi-scale polynomial autoregressive model; respectively. We find that the proposed forecasting method achieves 99% of the explained variance with reduced parsimony and high accuracy.

Original languageEnglish
Title of host publication2nd International Conference on Environmental and Computer Science, ICECS 2009
Pages175-178
Number of pages4
DOIs
StatePublished - 1 Dec 2009
Event2nd International Conference on Environmental and Computer Science, ICECS 2009 - Dubai, United Arab Emirates
Duration: 28 Dec 200930 Dec 2009

Publication series

Name2nd International Conference on Environmental and Computer Science, ICECS 2009

Conference

Conference2nd International Conference on Environmental and Computer Science, ICECS 2009
CountryUnited Arab Emirates
CityDubai
Period28/12/0930/12/09

Keywords

  • Forecasting
  • Multivariate polynomial
  • Wavelet analysis

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