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

Abstract:

Article Preview

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 single-step prediction is superior to that of multi-step prediction.

Info:

Periodical:

Edited by:

Zhixiang Hou

Pages:

233-236

DOI:

10.4028/www.scientific.net/AMM.128-129.233

Citation:

Y. L. Chen et al., "Research on the Magnitude Time Series Prediction Based on Wavelet Neural Network", Applied Mechanics and Materials, Vols. 128-129, pp. 233-236, 2012

Online since:

October 2011

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.