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 - 2010
Externally publishedYes
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
Country/TerritoryThailand
CityPhuket
Period9/01/1010/01/10

Keywords

  • Forecasting
  • Wavelet analysis

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