Optimal Design of Hierarchical Wavelet Networks for Time-series Forecasting

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Optimal Design of Hierarchical Wavelet Networks for Time-series Forecasting

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by Yuehui Chen , Bo Yang , Ajith Abraham

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BibTeX

@MISC{Chen_optimaldesign,
    author = {Yuehui Chen and Bo Yang and Ajith Abraham},
    title = {Optimal Design of Hierarchical Wavelet Networks for Time-series Forecasting},
    year = {}
}

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Abstract

Abstract. The purpose of this study is to identify the Hierarchical Wavelet Neural Networks (HWNN) and select important input features for each sub-wavelet neural network automatically. Based on the predefined instruction/operator sets, a HWNN is created and evolved using tree-structure based Extended Compact Genetic Programming (ECGP), and the parameters are optimized by Differential Evolution (DE) algorithm. This framework also allows input variables selection. Empirical results on benchmark time-series approximation problems indicate that theproposedmethodiseffectiveandefficient. 1

Keyphrases

time-series forecasting optimal design hierarchical wavelet network hierarchical wavelet neural network differential evolution empirical result select important input feature predefined instruction operator set extended compact genetic programming sub-wavelet neural network benchmark time-series approximation problem input variable selection

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