[1]郭小芳,李锋,宋晓宁.一种基于PCA的时间序列异常检测方法[J].江西师范大学学报(自然科学版),2012,(03):280-283.
　GUO Xiao-fang,LI Feng,SONG Xiao-ning.The Outlier Detection Approach for Multivariate Time Series Based on PCA Analysis[J].Journal of Jiangxi Normal University:Natural Science Edition,2012,(03):280-283.
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一种基于PCA的时间序列异常检测方法()
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《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2012年03期
页码:
280-283
栏目:
出版日期:
2012-05-01

文章信息/Info

Title:
The Outlier Detection Approach for Multivariate Time Series Based on PCA Analysis
作者:
郭小芳;李锋;宋晓宁
江苏科技大学计算机科学与工程学院,江苏镇江212003；江苏科技大学电子信息学院,江苏镇江212003
Author(s):
GUO Xiao-fang;  LI Feng;  SONG Xiao-ning
关键词:
多元时间序列; 主成分分析;  k-近邻; 异常检测
Keywords:
multivariate time series;  principal component analysis;  k-nearest neighbor;  outlier detection
分类号:
TP391
文献标志码:
A
摘要:
在 k-近邻局部异常检测算法的基础上,采用基于主成分分析的多元时间序列的降维方法,依据累积贡献率选择主成分序列,给出了一种效率较高的多元时间序列异常检测算法.实验结果表明:该算法可以较好地提高多元时间序列异常检测的效率
Abstract:
By means of cumulative contribution rate, dimension reduction method based on principal component analysis and principal components of multivariate time series was selected, an efficient multivariate time series outlier detection algorithm was provided based on the k-nearest neighbor local outlier detection algorithm was provide here,. the experimental results show that the algorithm can morely improve the efficiency of multivariate time series outlier detection.

参考文献/References:

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相似文献/References:

[1]郭小芳,李锋,刘庆华.一种有效的多元时间序列相似性度量算法分析[J].江西师范大学学报(自然科学版),2013,(01):56.
　GUO Xiao-fang,LI Feng,LIU Qing-hua.The Analysis for an Effective Algorithm of Similarity Measurement of Multivariate Time Series[J].Journal of Jiangxi Normal University:Natural Science Edition,2013,(03):56.

更新日期/Last Update: 1900-01-01