多元时间序列缺失数据填补方法

doi:10.3969/j.issn.1001-506X.2018.01.32

系统工程与电子技术 ›› 2018, Vol. 40 ›› Issue (1): 225-230.doi: 10.3969/j.issn.1001-506X.2018.01.32

多元时间序列缺失数据填补方法

李正欣, 张凤鸣, 王瑛, 陶茜, 李超

空军工程大学装备管理与无人机工程学院, 陕西西安 710051

出版日期:2018-01-08 发布日期:2018-01-08

Method of missing data imputation for multivariate time series

LI Zhengxin, ZHANG Fengming, WANG Ying, TAO Qian, LI Chao

Equipment Management and Unmanned Aerial Vehicle Engineering College, Air Force Engineering University, Xi’an 710051, China

Online:2018-01-08 Published:2018-01-08

摘要/Abstract

摘要：

多元时间序列是一种普遍存在的数据类型,受多种干扰因素的作用,序列中难免存在缺失数据,影响后续的分析处理。首先,针对存在缺失数据的序列,搜索与其同类的相似序列,构建训练集;然后,利用最小二乘支持向量机,分别进行多变量填补和单变量填补;第三,根据多变量和单变量填补结果的差异度,提出了一种组合阈值填补方法。最后,对所提方法进行了实验验证,结果表明,它具有较高的填补精度且适用于缺失数据较多的场合。

Abstract:

Multivariate time series is a common data type. However, due to many kinds of interference factors, missing data are inevitable, which affects data process and analysis. Firstly, for the series containing missing data, similar series that are of the same class with it are searched to form the training set. Secondly, making use of least squares support vector machine, missing data are filled by univariate and multivariate filling methods respectively. Then, according to the difference between the filling results of univariate and multivariate filling methods, a combined method is proposed. Finally, extensive experiments are conducted. The results show that the proposed method can fill missing data precisely, and can be used in the case where the amount of missing data is relatively large.

李正欣, 张凤鸣, 王瑛, 陶茜, 李超. 多元时间序列缺失数据填补方法[J]. 系统工程与电子技术, 2018, 40(1): 225-230.

LI Zhengxin, ZHANG Fengming, WANG Ying, TAO Qian, LI Chao. Method of missing data imputation for multivariate time series[J]. Systems Engineering and Electronics, 2018, 40(1): 225-230.

多元时间序列缺失数据填补方法

Method of missing data imputation for multivariate time series

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价