Monthly anchovy catches forecasting using wavelet polynomial autoregression

NIBALDO RODRIGUEZ AGURTO, Eleuterio Yanez

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

Abstract

In this paper, a multivariate polynomial (MP) model combined with wavelet analysis is proposed to improve the accuracy and parsimony of 1-month ahead forecasting of monthly anchovy catches in northern Chile. The proposed forecasting model is based on the decomposition the raw data set into low frequency (LF) and high frequency (HF) components by using stationary wavelet transform. In wavelet domain, the LF component and HF component are predicted with a linear autoregressive model and multiscale polynomial autoregressive model; respectively. We find that the proposed forecasting method achieves 99% of the explained variance with reduced parsimony and high accuracy. Besides, the proposed forecaster proves to be more accurate and performs better than the multilayer perceptron neural network model.

Original languageEnglish
Title of host publication3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
Pages126-129
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

  • Autoregression
  • Forecasting
  • Wavelet analysis

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