Observatorio de Investigación y Desarrollo Tecnológico


  • POLIBITS: INSTITUTO POLITECNICO NACIONAL//, MEXICO

    Volumen: 1, Numero: 1, Páginas: PP. 49-53

    HAAR WAVELET NEURAL NETWORK FOR MULTI-STEP-AHEAD ANCHOVY CATCHES FORECASTING

    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.


    Keywords


    Neural network, wavelet analysis, forecasting model.


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