Research on Methods of Filling Missing Data for Multivariate Time Series

被引：0
|
作者：
Li, Zheng-Xin [1 ]
Wu, Shi-Hui [1 ]
Li, Chao [1 ]
Zhang, Yu [1 ]
机构：
[1] Air Force Engn Univ, Equipment Management & Safety Engn Coll, Xian, Shaanxi, Peoples R China
来源：
2017 IEEE 2ND INTERNATIONAL CONFERENCE ON BIG DATA ANALYSIS (ICBDA) | 2017年
关键词：
multivariate time series; missing data; similarity search; data mining; least squares support vector machine;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Multivariate time series (MTS) exist in many applications. Due to all kinds of interference factors, missing data in MTS is inevitable. Aiming at this problem, a filling method based on least squares support vector machine (LSSVM) is proposed. Firstly, for the series containing missing data, similar series are searched, and its results are viewed as the training set. Secondly, to make use of the correlation among the variables in MTS, LSSVM is trained. Thirdly, LSSVM is used to fill missing data. Finally, experiments are conducted to check the validity of the proposed method. The results show it can obtain good filling effect, even when the length of the missing data segment is relative large.
引用
收藏
页码：387 / 390
页数：4
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