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A New Method Detecting Abrupt Change Base on Moving Cut Data-Permutation Entropy

Received: 19 June 2018     Published: 20 June 2018
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Abstract

Permutation entropy is an effective index which can be used to describe the dynamic complexity of a time series, and it can effectively enlarge the small changes of a sequence. In this paper, the moving cut data-permutation entropy, a new method detecting abrupt change is raised by combining the permutation entropy method with the moving cut data technology. Different moving window scales are selected to analyze the mutational detection of linear and nonlinear time series via the new method respectively. The effect of peak noise and white Gaussian noise on this new method in nonlinear time series constructed by Lorenz equation and random sequence was studied. The results show that the moving cut data-permutation entropy method has strong anti-noise ability, which is able to precisely identify the mutational point of both the linear and nonlinear time series, and almost independent the scale of window and the length of sequence.

Published in American Journal of Applied Mathematics (Volume 6, Issue 2)
DOI 10.11648/j.ajam.20180602.16
Page(s) 62-70
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Permutation Entropy, Moving Cut Data, Dynamical Structure, Mutational Detection

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Cite This Article
  • APA Style

    Luo Wenxiang, Wan Li, Lai Simin. (2018). A New Method Detecting Abrupt Change Base on Moving Cut Data-Permutation Entropy. American Journal of Applied Mathematics, 6(2), 62-70. https://doi.org/10.11648/j.ajam.20180602.16

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    ACS Style

    Luo Wenxiang; Wan Li; Lai Simin. A New Method Detecting Abrupt Change Base on Moving Cut Data-Permutation Entropy. Am. J. Appl. Math. 2018, 6(2), 62-70. doi: 10.11648/j.ajam.20180602.16

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    AMA Style

    Luo Wenxiang, Wan Li, Lai Simin. A New Method Detecting Abrupt Change Base on Moving Cut Data-Permutation Entropy. Am J Appl Math. 2018;6(2):62-70. doi: 10.11648/j.ajam.20180602.16

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  • @article{10.11648/j.ajam.20180602.16,
      author = {Luo Wenxiang and Wan Li and Lai Simin},
      title = {A New Method Detecting Abrupt Change Base on Moving Cut Data-Permutation Entropy},
      journal = {American Journal of Applied Mathematics},
      volume = {6},
      number = {2},
      pages = {62-70},
      doi = {10.11648/j.ajam.20180602.16},
      url = {https://doi.org/10.11648/j.ajam.20180602.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20180602.16},
      abstract = {Permutation entropy is an effective index which can be used to describe the dynamic complexity of a time series, and it can effectively enlarge the small changes of a sequence. In this paper, the moving cut data-permutation entropy, a new method detecting abrupt change is raised by combining the permutation entropy method with the moving cut data technology. Different moving window scales are selected to analyze the mutational detection of linear and nonlinear time series via the new method respectively. The effect of peak noise and white Gaussian noise on this new method in nonlinear time series constructed by Lorenz equation and random sequence was studied. The results show that the moving cut data-permutation entropy method has strong anti-noise ability, which is able to precisely identify the mutational point of both the linear and nonlinear time series, and almost independent the scale of window and the length of sequence.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - A New Method Detecting Abrupt Change Base on Moving Cut Data-Permutation Entropy
    AU  - Luo Wenxiang
    AU  - Wan Li
    AU  - Lai Simin
    Y1  - 2018/06/20
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ajam.20180602.16
    DO  - 10.11648/j.ajam.20180602.16
    T2  - American Journal of Applied Mathematics
    JF  - American Journal of Applied Mathematics
    JO  - American Journal of Applied Mathematics
    SP  - 62
    EP  - 70
    PB  - Science Publishing Group
    SN  - 2330-006X
    UR  - https://doi.org/10.11648/j.ajam.20180602.16
    AB  - Permutation entropy is an effective index which can be used to describe the dynamic complexity of a time series, and it can effectively enlarge the small changes of a sequence. In this paper, the moving cut data-permutation entropy, a new method detecting abrupt change is raised by combining the permutation entropy method with the moving cut data technology. Different moving window scales are selected to analyze the mutational detection of linear and nonlinear time series via the new method respectively. The effect of peak noise and white Gaussian noise on this new method in nonlinear time series constructed by Lorenz equation and random sequence was studied. The results show that the moving cut data-permutation entropy method has strong anti-noise ability, which is able to precisely identify the mutational point of both the linear and nonlinear time series, and almost independent the scale of window and the length of sequence.
    VL  - 6
    IS  - 2
    ER  - 

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Author Information
  • School of Mathematics and Information Science, Guangzhou University, Guangzhou, China

  • School of Mathematics and Information Science, Guangzhou University, Guangzhou, China

  • School of Mathematics and Information Science, Guangzhou University, Guangzhou, China

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