Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (35): 145-147.DOI: 10.3778/j.issn.1002-8331.2008.35.044

• 数据库、信号与信息处理 • Previous Articles     Next Articles

Effective time series outlier detection algorithm based on segmentation

ZHOU Da-zhuo1,2,LIU Yue-fen2,MA Wen-xiu2   

  1. 1.School of Management,Tianjin University,Tianjin 300072,China
    2.Computer Center,Hebei University of Economics and Trade,Shijiazhuang 050061,China
  • Received:2007-12-20 Revised:2008-03-20 Online:2008-12-11 Published:2008-12-11
  • Contact: ZHOU Da-zhuo

时间序列异常检测

周大镯1,2,刘月芬2,马文秀2   

  1. 1.天津大学 管理学院,天津 300072
    2.河北经贸大学 计算机中心,石家庄 050061
  • 通讯作者: 周大镯

Abstract: A new time series outlier detection algorithm of high-efficiency is proposed for the foundation of k-nearest local outlier detection algorithm based on segmentation.Firstly,series important point as segmentation point can compress high-proportionally time series data in this algorithm;Secondly,the outlier pattern of time series can be detected by local outlier detection technique.Experimental results on electrocardiogram(ECG) data show that the algorithm is effective and reasonable.

Key words: time series, outlier pattern, local outlier factor, series important point

摘要: 在k-近邻局部异常检测算法的基础上,结合时间序列的分割方法,提出了一种高效的时间序列异常检测算法。该算法首先把序列重要点作为数据的分割点,对时间序列数据进行高比例压缩;其次利用局部异常检测方法检测出时间序列中的异常模式。通过心电图(ECG)数据实验验证了算法的有效性和合理性。

关键词: 时间序列, 异常模式, 局部异常因子, 序列重要点