World oil price forecasting based on wavelet analyze and chaotic time series technology
小波分频技术和混沌时间序列在国际石油价格预测中的应用

A new algorithm for worm oil price chaotic time series prediction based on wavelet analyze and Volterra self adaptive filter method is presented. Firstly, the original oil price time series is decomposed as the measurement coefficients and wavelet coefficients by utilizing the stationary wavelet transform.Secondly, the coefficients are predicted with a Volterra adaptive filter in their reconstituted phase spaces based on the chaotic time series method. Finally the predictions of the coefficients are acquired by the inverse wavelet transform. The result shows that the proposed method can capture the dynamics of the nonlinear systems series effectively.