Earticle

초록

영어

In common time series analysis methods, the prediction accuracy of the low-order model is poor, and the high-order model is difficult to calculate. Therefore, in this paper, we improve the construction process of the Kalman filtering model, and apply it into time series analysis. The concrete implementation for the improved method is to construct the low-order model with the ARMA method and intercept sufficient delay states, to deduce the state equation and measurement equation of the Kalman filtering model. As the experimental results show that,the improved Kalman filtering model can not only simplify the derivation of the state equation and measurement equation, but also achieve ideal prediction accuracy, the largest prediction error of the experimental data is -0.15%.

목차

Abstract
 1. Introduction
 2. Construction of the Improved Kalman Filtering Model
  2.1 Construction Time Series Low-Order Model with the ARMA Method
  2.2 Construction of Kalman Model Based on the ARMA Low-Order Model 
 3. Experimental Results and Analysis
 4. Conclusion
 References

키워드

저자

• Yang Zhizhong [ Harbin Institute of Technology, School of Management, Harbin, 150001, China ]
• Xi Bao [ Harbin Institute of Technology, School of Management, Harbin, 150001, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605