Multiscale functional autoregressive model for monthly sardines catches forecasting

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

1 Scopus citations

Abstract

In this paper, we use a functional autoregressive (FAR) model combined with multi-scale stationary wavelet decomposition technique for one-month-ahead monthly sardine catches forecasting in northern area of Chile (18 o 21S∈-∈24 o S).The monthly sardine catches data were collected from the database of the National Marine Fisheries Service for the period between 1 January 1973 and 30 December 2007. 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, the trend component and residual component are predicted by use a linear autoregressive model and FAR model; respectively. 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. Besides, is showed that the wavelet-autoregressive forecaster is more accurate and performs better than both multilayer perceptron neural network model and FAR model.

Original languageEnglish
Title of host publicationMICAI 2009
Subtitle of host publicationAdvances in Artificial Intelligence - 8th Mexican International Conference on Artificial Intelligence, Proceedings
Pages189-200
Number of pages12
DOIs
StatePublished - 1 Dec 2009
Event8th Mexican International Conference on Artificial Intelligence, MICAI 2009 - Guanajuato, Mexico
Duration: 9 Nov 200913 Nov 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5845 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th Mexican International Conference on Artificial Intelligence, MICAI 2009
CountryMexico
CityGuanajuato
Period9/11/0913/11/09

Keywords

  • Autoregression
  • Forecasting
  • Wavelet decomposition

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