ISSN 1000-1239 CN 11-1777/TP


• 人工智能 • 上一篇    下一篇


苏卫星1,2 朱云龙1 刘 芳3 胡琨元1   

  1. 1(中国科学院沈阳自动化研究所 沈阳 110016) 2(中国科学院大学 北京 100049) 3(华晨汽车工程研究院 沈阳 110027) (
  • 出版日期: 2014-04-15

Outliers and Change-Points Detection Algorithm for Time Series

Su Weixing1,2, Zhu Yunlong1, Liu Fang3, and Hu Kunyuan1   

  1. 1(Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016) 2(University of Chinese Academy of Sciences, Beijing 100049) 3(Brilliance Automobile Engineering Research Institute, Shenyang 110027)
  • Online: 2014-04-15

摘要: 针对传统突变点检测算法具有大延时的问题以及实际数据中同时含有突变点、异常点的实际情况,提出一种基于小波变换有效分数向量的异常点、突变点检测算法.该方法通过引入有效分数向量作为检测统计量,有效避免了传统检测统计量随着数据增多而无限增大的缺点;提出利用小波分析统计量的办法,有效地克服了传统突变点检测算法中存在大延时的缺陷;利用李氏指数及小波变换的关系,实现了在一个检测框架内同时在线检测异常点以及突变点,使得该检测算法更符合突变点及异常点同时存在的实际情况.仿真实验和性能比较结果证明了提出的异常点、突变点检测算法具有一定的有效性和实用性.

关键词: 异常点, 突变点, 小波变换, Lipschitz指数, 时间序列

Abstract: Because the conventional change-points detection method exists the shortages on time delay and inapplicability for the time series mingled with outliers in the practical applications, an outlier and change-point detection algorithm for time series, which is based on the wavelet transform of the efficient score vector, is proposed in this paper. The algorithm introduces the efficient score vector to solve the problem of the conventional detection method that statistics often increass infinitely with the number of data added during the process of detection, and proposes a strategy of analyzing the statistics by using wavelet in order to avoid the serious time delay. In order to distinguish the outlier and change-point during the detection process, we propose a detecting framework based on the relationship between Lipschitz exponent and the wavelet coefficients, by which both outlier and change-point can be detected out meanwhile. The advantage of this method is that the detection effect is not subject to the influence of the outlier. It means that the method can deal with the time series containing both outliers and change-points under actual operating conditions and it is more suitable for the real application. Eventually, the effectiveness and practicality of the proposed detection method have been proved through simulation results.

Key words: outlier, change-point, wavelet transform, Lipschitz exponent, time series