Earticle

초록

영어

This paper uses phase space reconstruction as the basis of the multi – input nonlinear method. It is obvious that for a system with multiple variables, it is necessary to choose the involved variable before reconstruction, and identify the reconstruction parameter, after determining the reconstructed variable, in order to complete the basic reconstruction. Therefore, based on the selection of the nonlinear correlation, this paper introduces the method for choosing the correct input variable at first , and then introduces some commonly used methods to identify the reconstruction parameter, such as mutual information method, auto – correlation method and average displacement method etc.. Furthermore, it specially introduces the C – C method. By carrying out the multivariate combination forecasting simulation for time series of the Lorentz Equation and comparing the reconstruction phase diagram of the multivariate phase space, this paper verifies the accuracy of selecting reconstructed input vector based on nonlinear correlation and the effectiveness of using C- C method to decide the reconstruction parameter.

목차

Abstract
 1. Introduction
 2. Model Approach
  2.1. Univariate Phase Space Reconstruction
  2.2. Multivariate Phase Space Reconstruction
 3. Algorithm and Implementation
 4. Multivariate Forecasting Simulations
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Jianming Sun [ Harbin University of Commerce, Harbin ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605