Haar Wavelet Neural Network for Multi-step-ahead Anchovy Catches Forecasting
Author
Rodríguez-Agurto, José NibaldoBravo, Gabriel
Barba-Maggi, Lida
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...
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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 (18oS-24oS) 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.
Index Terms—Neural network, wavelet analysis, forecasting
model.
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Date de publicación
2014Metadata
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