文章摘要
引用本文:董红玉,陈晓云.基于改进ADPP的多变量时间序列异常检测[J].福州大学学报(自然科学版),2016,44(2):164~169
基于改进ADPP的多变量时间序列异常检测
Outlier detection based on improved ADPP for multivariate time series
  
DOI:
中文关键词: 多变量时间序列  异常检测  张量相似性度量   k -近邻图
英文关键词: multivariate time series  outlier detection  tensor similarity measure   k -neighbor graph
基金项目:
作者单位
董红玉 福州大学数学与计算机科学学院福建 福州 350116 
陈晓云 福州大学数学与计算机科学学院福建 福州 350116 
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中文摘要:
      针对多变量时间序列异常检测问题进行研究,提出基于改进ADPP的多变量时间序列异常检测算法IADPP. IADPP算法引入适用于多变量时间序列的张量相似性度量 S SOTPCA,并以此相似性度量构造序列集的 k -近邻图,在构造的 k -近邻图上计算多变量时间序列的异常系数. 研究结果表明,IADPP算法克服了原有ADPP算法不支持多变量时间序列和要求密度均匀的缺陷,取得了较好的检测结果.
英文摘要:
      We study the outlier detection for multivariate time series,and an approach of outlier detection for multivariate time series based on improved ADPP-IADPP is proposed. IADPP algorithm introduces tensor similarity measure S SOTPCA supporting for multivariate time series,and constructs the k - neighbor graph about the sequence set. Then,we calculate the outlier coefficient of multivariate time series on k -neighbor graph .The research results show that the proposed method overcomes the disadvantages that original ADPP does not support multivariate time series and requests uniform density,IADPP algorithm achieves a better detection results.
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