Monthly bigeye tuna catches forecasting usingwavelet functional autoregression

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

Abstract

In this paper, the aim is to apply a functional autoregressive (FAR) model combined with multiscale wavelet analysis for monthly bigeye tuna catches forecasting in the ocean ecosystem of the equatorial Indian ocean. Wavelet technique performs a time-frequency analysis of a time series, which permits to decompose the raw time series into trend and residual components. In wavelet domain, the trend component and residual component are forecasted with a linear autoregressive model and a FAR 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 publication3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
Pages67-70
Number of pages4
DOIs
StatePublished - 18 May 2010
Event3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010 - Phuket, Thailand
Duration: 9 Jan 201010 Jan 2010

Publication series

Name3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010

Conference

Conference3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
CountryThailand
CityPhuket
Period9/01/1010/01/10

Keywords

  • Forecasting
  • Wavelet analysis

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