Application of Wavelet Neural Network for Chaos Time Series Prediction

A method for chaotic time series prediction Based on wavelet neural network is discussed by using the theory of phase space reconstruction. The minimum embedding dimensions was used as the number of input nodes. The lorenz chaotic time series and hénon series are used to verify the proposed method. It is found that the proposed wavelet neural network performs well in the chaotic time series prediction, and its results agree well with experimental data with high accuracy over wavelet network without phase space reconstruction.