Research on the Magnitude Time Series Prediction Based on Wavelet Neural Network

摘要：

Based on the fundamental principles of the wavelet analysis combining with BP neural network, the paper can obtain the minimum embedding dimension and delay time. According to the chaos theory, the phase space of the magnitude time series can be reconstructed by Takens theorem. The paper uses wavelet neural network to train and test the nonlinear magnitude time series in the reconstructed phase space. The simulation results show that the predictive effect of the magnitude time series is remarkable and the predictive performance of singlestep prediction is superior to that of multi-step prediction.

关键词： wavelet neural network phase space reconstruction time series magnitude prediction

作者: Chen Yanlan Chen Yi Huang Qing

作者单位: Guilin University of Electronic Technology, Guilin, 541004, China

会议类型: 国际会议

会议名称: 4th International Conference on Measuring Technology and Mechatronics Automation(第四届检测技术与机电自动化国际会议 ICMTMA 2012)

会议地点: 三亚

会议语种:英文

页码: 233-236

在线出版日期: 2012-01-06（万方平台首次上网日期，不代表论文的发表时间）