Real-Time Segmenting Time Series Data

  • Aiguo Li
  • Shengping He
  • Zheng Qin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2642)


There has been increased interest in time series data mining recently. In some cases, approaches of real-time segmenting time series are necessary in time series similarity search and data mining, and this is the focus of this paper. A real-time iterative algorithm that is based on time series prediction is proposed in this paper. Proposed algorithm consists of three modular steps. (1) Modeling: the step identifies an autoregressive moving average (ARMA) model of dynamic processes from a time series data; (2) prediction: this step makes k steps ahead prediction based on the ARMA model of the process at a crisp time point. (3) Change-points detection: the step is what fits a piecewise segmented polynomial regressive model to the time series data to determine whether it contains a new change point. Finally, high performance of the proposed algorithm is demonstrated by comparing with Guralnik-Srivastava algorithm.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Aiguo Li
    • 1
  • Shengping He
    • 1
  • Zheng Qin
    • 1
  1. 1.Department of Computer ScienceXi’an Jiaotong UniversityXi’an, ShaanxiChina

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