ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development

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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

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