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Wavelet Autoregressive Model for Monthly Sardines Catches Forecasting Off Central Southern Chile

  • Nibaldo Rodriguez
  • Jose Rubio
  • Eleuterio Yañez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

In this paper, we use multi-scale stationary wavelet decomposition technique combined with a linear autoregressive model for one-month-ahead monthly sardine catches forecasting off central southern Chile.The monthly sardine catches data were collected from the database of the National Marine Fisheries Service for the period between 1 January 1964 and 30 December 2008. The proposed forecasting strategy is to decompose the raw sardine catches data set into trend component and residual component by using multi-scale stationary wavelet transform. In wavelet domain, both the trend component and the residual component are independently predicted using a linear autoregressive model. Hence, proposed forecaster is the co-addition of two predicted components. We find that the proposed forecasting method achieves a 99% of the explained variance with a reduced parsimonious and high accuracy.

Keywords

forecasting wavelet decomposition autoregression 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nibaldo Rodriguez
    • 1
  • Jose Rubio
    • 1
  • Eleuterio Yañez
    • 1
  1. 1.Pontificia Universidad Católica de ValparaísoChile

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