Dynamic Information Extraction for the Big Data

被引：0
|
作者：
Jin, Xue-bo [1 ,2 ]
Dou, Chao [2 ]
机构：
[1] State Key Lab Digital Publishing Technol, Beijing, Peoples R China
[2] Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing 100048, Peoples R China
来源：
PROCEEDINGS OF THE 2016 12TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2016年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
For the practical big time series data, it is the important step to eliminate the noise and get the dynamic information of the time series data. For the series data, the extraction model and the transform model are given in this paper, and the sampling interval of the models is discussed to guarantee the estimated system convergence. The dynamic information is analyzed according to the estimated dynamic characteristic. The experiment results show that for the time series data, the dynamic information extracted by Kalman filter is feasible, and can be used to analyze the trend characteristic effectively.
引用
收藏
页码：1027 / 1030
页数：4
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