Haar Wavelet Neural Network for Multi-step-ahead Anchovy Catches Forecasting

 

Nibaldo Rodriguez^1*, Gabriel Bravo², and Lida Barba³

 

¹ Pontificia Universidad Católica de Valparaíso, Av. Brasil 2241, Chile. *Corresponding author (e-mail: nibaldo.rodriguez@ucv.cl).

² Universidad San Sebastián, Concepción, Chile. (e-mail: gabo.bravoro@hotmail.com).

³ Universidad Nacional de Chimborazo, Av. Antonio José de Sucre, Riobamba, Ecuador. (e-mail: lbarba@unach.edu.ec).

 

Manuscript received on August 7, 2014
Accepted for publication on September 22, 2014
Published on November 15, 2014.

 

Abstract

This paper proposes a hybrid multi-step-ahead forecasting model based on two stages to improve pelagic fish-catch time-series modeling. In the first stage, the Fourier power spectrum is used to analyze variations within a time series at multiple periodicities, while the stationary wavelet transform is used to extract a high frequency (HF) component of annual periodicity and a low frequency (LF) component of inter-annual periodicity. In the second stage, both the HF and LF components are the inputs into a single-hidden neural network model to predict the original non-stationary time series. We demonstrate the utility of the proposed forecasting model on monthly anchovy catches time-series of the coastal zone of northern Chile (18°S-24°S) for periods from January 1963 to December 2008. Empirical results obtained for 7-month ahead forecasting showed the effectiveness of the proposed hybrid forecasting strategy.

Key words: Neural network, wavelet analysis, forecasting model.

 

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Acknowledgment

This research was partially supported by the Chilean National Science Fund through the project Fondecyt-Regular 1131105 and by the VRIEA of the Pontificia Universidad Católica de Valparaíso.

 

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