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Multiscale wavelet decomposition based functional autoregression for monthly anchovy catches forecasting

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Autor Rodriguez N.
Autor Yanez E.
Fecha Ingreso 2014-04-05T00:17:05Z
Fecha Disponible 2014-04-05T00:17:05Z
Fecha en Repositorio 2014-04-04
dc.identifier 10.1109/ICICISYS.2009.5357795
dc.description.abstract In this paper, a multi-scale stationary wavelet decomposition technique combined with functional auto-regression is used to improve the prediction accuracy and parsimony of anchovy monthly catches forecasting in area north of Chile (18 21'S-24 S). The general idea behind this approach is to decompose the observed anchovy catches data into low frequency (LF) component and high frequency (HF) component by using stationary wavelet transform and to separately forecast each frequency component. The forecasting strategy was evaluated for a period of 42 years, starting from 1-Jun-1963 to 31-Dec-2007 and we find that the proposed forecasting method achieves a 98% of the explained variance with a reduced parsimony 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 functional autoregressive model. ©2009 IEEE. en_US
dc.source Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Link Descarga dc.source.uri
Title dc.title Multiscale wavelet decomposition based functional autoregression for monthly anchovy catches forecasting en_US
Tipo dc.type Conference Paper
dc.description.keywords Auto regressive models; Auto-regressive; Autoregression; Decomposition technique; Forecasting methods; Frequency components; High frequency components; Low frequency; Multi-layer perceptron neural networks; Multi-scale wavelet decomposition; Multiscales; Prediction accuracy; Stationary wavelet; Stationary wavelet transforms; Forecasting; Intelligent computing; Intelligent systems; Neural networks; Regression analysis; Wavelet decomposition en_US

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