Title :
Notice of Retraction
Semi-parametric regression model prediction method based on empirical mode decomposition
Author :
Zhang Qingjie ; Zhu Huayong ; Shen Lincheng
Author_Institution :
Coll. of Mechatron. Eng. & Autom., Nat. Univ. of Defense Technol., Changsha, China
Abstract :
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Semi-parametric regression model prediction method based on empirical mode decomposition was studied in this paper. Firstly, basic idea of the empirical mode decomposition was introduced, and the improved algorithm was proposed to mitigate the end effect in the iterative shift process. Secondly, least squares method was employed to estimate the parameter β based on the trend component of empirical mode decomposition, and the non-parametric g(·) was estimated through building the AR models of the intrinsic mode functions. The vector matrix was computed by Yule-Walker method. Finally, time series prediction of two nonlinear systems was analyzed based on the semi-parametric regression model. The results show that the proposed model predictive method is fit for nonlinear and non-stationary time series estimate.
Keywords :
estimation theory; iterative methods; least squares approximations; matrix algebra; regression analysis; time series; vectors; AR model; Yule-Walker method; empirical mode decomposition; intrinsic mode function; iterative shift process; least squares method; nonlinear system; nonlinear time series estimation; nonstationary time series estimation; parameter estimation; semi-parametric regression model prediction method; time series prediction; vector matrix; Biological system modeling; Frequency; Iterative algorithms; Iterative methods; Least squares methods; Nonlinear systems; Prediction methods; Predictive models; Spline; Time series analysis; Empirical Mode Decomposition; end effect; least square polynomial; mirror extend;
Conference_Titel :
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-5845-5
DOI :
10.1109/ICACC.2010.5486660
