Wavelet autoregressive model for monthly sardines catches forecasting off central southern Chile

Nibaldo Rodriguez, Jose Rubio, Eleuterio Yañez

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

1 Scopus citations

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.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Proceedings
Pages654-663
Number of pages10
DOIs
StatePublished - 2011
Externally publishedYes
Event16th Iberoamerican Congress on Pattern Recognition, CIARP 2011 - Pucon, Chile
Duration: 15 Nov 201118 Nov 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7042 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th Iberoamerican Congress on Pattern Recognition, CIARP 2011
Country/TerritoryChile
CityPucon
Period15/11/1118/11/11

Keywords

  • autoregression
  • forecasting
  • wavelet decomposition

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