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"Combining Neural Network Forecasts on Wavelet-Transformed Time Series"^^ .
"We discuss a simple strategy aimed at improving neural network prediction accuracy, based on the combination of predictions at varying resolution levels of the domain under investigation (here: time series). First, a wavelet transform is used to decompose the time series into varying scales of temporal resolution. The latter provide a sensible decomposition of the data so that the underlying temporal structures of the original time series become more tractable. Then, a Dynamical Recurrent Neural Network (DRNN) is trained on each resolution scale with the temporal-recurrent backpropagation (TRBP) algorithm. By virtue of its internal dynamic, this general class of dynamic connectionist network approximates the underlying law governing each resolution level by a system of nonlinear difference equations. The individual wavelet scale forecasts are afterwards recombined to form the current estimate. The predictive ability of this strategy is assessed with the sunspot series."^^ .
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"Connection Science"^^ .
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"Alex"^^ .
"Aussem"^^ .
"Alex Aussem"^^ .
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"Fionn"^^ .
"Murtagh"^^ .
"Fionn Murtagh"^^ .
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"Combining Neural Network Forecasts on Wavelet-Transformed Time Series (Postscript)"^^ .
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"ConnectScience.ps"^^ .
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"HTML Summary of #551 \n\nCombining Neural Network Forecasts on Wavelet-Transformed Time Series\n\n" .
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"Artificial Intelligence" .
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"Dynamical Systems" .
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"Machine Learning" .
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"Statistical Models" .
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