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

Luo Wenxiang; Wan Li; Lai Simin

doi:doi:10.11648/j.ajam.20180602.16

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

Luo Wenxiang, Wan Li, Lai Simin

Published in American Journal of Applied Mathematics (Volume 6, Issue 2)

Received: 19 June 2018 Accepted: 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), 2024. Published by Science Publishing Group

Keywords

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

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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

Luo Wenxiang

School of Mathematics and Information Science, Guangzhou University, Guangzhou, China
Wan Li

School of Mathematics and Information Science, Guangzhou University, Guangzhou, China
Lai Simin

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

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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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download