Multiscale polynomial autoregressive model for monthly sardines catches forecasting

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

Abstract

The aim of this paper is to find a model to forecast 1-month ahead monthly sardines catches using a multivariate polynomial model combined with multi-scale stationary wavelet decomposition. The observed monthly sardines catches are decomposed into various sub-series employing wavelet decomposition and then appropriate sub-series are used as inputs to the autoregressive forecasting model. The forecasting strategy parameters are estimated using the least squares method and we find that the proposed forecaster achieves 99% of the explained variance with a MAPE below 7.6%.

Original languageEnglish
Title of host publicationICCIT 2009 - 4th International Conference on Computer Sciences and Convergence Information Technology
Pages1524-1528
Number of pages5
DOIs
StatePublished - 1 Dec 2009
Event4th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2009 - Seoul, Korea, Republic of
Duration: 24 Nov 200926 Nov 2009

Publication series

NameICCIT 2009 - 4th International Conference on Computer Sciences and Convergence Information Technology

Conference

Conference4th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2009
CountryKorea, Republic of
CitySeoul
Period24/11/0926/11/09

Keywords

  • Forecasting
  • Regression
  • Wavelet analysis

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