Combining nonlinear additive autoregression with multiscale wavelet for monthly anchovy catches forecasting

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

Abstract

In this paper, a nonlinear additive autoregressive model combined with multiscale stationary wavelet transform is used to improve the accuracy and parsimony of one-monthahead forecasting of monthly anchovy catches in northern Chile (180 21′S-240 S). The general idea of the proposed forecasting model is to decompose the raw data set into trend and residual components by using SWT. In wavelet domain, the trend component and residual component are predicted with a linear autoregressive (AR) model and nonlinear additive autoregressive (NAAR) model; respectively. Hence, the proposed forecast is the co-addition of two predicted components. Data on monthly anchovy catches are available for a period of 44 years, from 1-Jun-1963 to 31-Dec-2007. We find that the proposed forecasting method achieves 99% of the explained variance with reduced parsimony and high accuracy. Besides, the wavelet-autoregressive forecaster proves to be more accurate and performs better than the multilayer perceptron (MLP) neural network model and NAAR model.

Original languageEnglish
Title of host publicationICCIT 2009 - 4th International Conference on Computer Sciences and Convergence Information Technology
Pages1223-1228
Number of pages6
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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