| 一种基于 PCA 的时间序列异常检测方法 |
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| 引用本文： | 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(3):280-283. |
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| 作者姓名： | 郭小芳 李锋 宋晓宁 |
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| 作者单位： | 1. 江苏科技大学计算机科学与工程学院,江苏镇江212003
2. 江苏科技大学电子信息学院,江苏镇江212003 |
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| 基金项目： | 国家自然科学基金(51008143)资助项目 |
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| 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
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| 关 键 词： | 多元时间序列 主成分分析 k-近邻 异常检测 |
The Outlier Detection Approach for Multivariate Time Series Based on PCA Analysis |
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| 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 Sciences Edition),2012(3):280-283. |
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| Authors: | GUO Xiao-fang LI Feng SONG Xiao-ning |
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| Institution: | 1(1.School of Computer Science and Engineering,Jiangsu University of Science and Technology,Zhenjiang Jiangsu 212003,China;2.School of Electronics and Information,Jiangsu University of Science and Technology,Zhenjiang Jiangsu 212003,China) |
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| 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. |
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| Keywords: | multivariate time series principal component analysis k-nearest neighbor outlier detection |
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