Optimal design of hierarchical wavelet networks for time-series forecasting.
Yuehui Chen, Bo Yang, Ajith Abraham · 2006
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