Earticle

초록

영어

Most time series big data is with noise and uncertain. To abstract the key information effectively and quickly, the estimation is one of the feasible methods for the uncertain big data. The Kalman filter with adaptive method by part of samples can give the high dimensional characteristics, reduce the computing cost and data uncertainty, but encounter the irregular estimation. The number of sample and the performance of the abstracted information have the tradeoff, which means we can use the suitable number of sample to abstract the key information of the series data. This paper discusses how to find the suitable sampling points for the time series data and the simulations show that the key dynamic information of time series big data can be guaranteed with the compression amount number of sample data.

목차

Abstract
 1. Introduction
 2. The Adaptive Dynamic Model
 3. The Estimation Method for the Series Big Data
 4. Simulation Results
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Miao Bei-bei [ School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048, China ]
• Jin Xue-bo [ School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048, China ]

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