IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v137y2020ics096007792030268x.html
   My bibliography  Save this article

Measuring time series based on multiscale dispersion Lempel–Ziv complexity and dispersion entropy plane

Author

Listed:
  • Mao, Xuegeng
  • Shang, Pengjian
  • Xu, Meng
  • Peng, Chung-Kang

Abstract

In this paper, we propose dispersion Lempel–Ziv complexity and combine it with dispersion entropy to construct complexity-entropy plane. This measure aims to identify time series with different properties. These two quantities take advantage of nonlinear symbolic representation and quantify the complexity of series. In addition, they are relatively stable for different parameters and robust against white noise with different levels. The complexity-entropy plane is able to identify nonlinear chaotic maps and distinguish them from stochastic process. Also, the multiscale features of signals are detected, suggesting the underlying dynamics. This technique is effective to track the dynamical changes of heart rate time series and to characterize different pathologic states. It can also prove that aging and disease correspond to the loss of complexity.

Suggested Citation

  • Mao, Xuegeng & Shang, Pengjian & Xu, Meng & Peng, Chung-Kang, 2020. "Measuring time series based on multiscale dispersion Lempel–Ziv complexity and dispersion entropy plane," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    • Handle: RePEc:eee:chsofr:v:137:y:2020:i:c:s096007792030268x
    DOI: 10.1016/j.chaos.2020.109868
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096007792030268X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.109868?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Zhengli & Shang, Pengjian, 2018. "Generalized information entropy analysis of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 1170-1185.
    2. Osvaldo Rosso & Felipe Olivares & Luciano Zunino & Luciana Micco & André Aquino & Angelo Plastino & Hilda Larrondo, 2013. "Characterization of chaotic maps using the permutation Bandt-Pompe probability distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(4), pages 1-13, April.
    3. S. Zozor & D. Mateos & P. Lamberti, 2014. "Mixing Bandt-Pompe and Lempel-Ziv approaches: another way to analyze the complexity of continuous-state sequences," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(5), pages 1-12, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wan, Li & Ling, Guang & Guan, Zhi-Hong & Fan, Qingju & Tong, Yu-Han, 2022. "Fractional multiscale phase permutation entropy for quantifying the complexity of nonlinear time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    2. Li, Yuxing & Geng, Bo & Jiao, Shangbin, 2022. "Dispersion entropy-based Lempel-Ziv complexity: A new metric for signal analysis," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    3. Gu, Danlei & Lin, Aijing & Lin, Guancen, 2022. "Sleep and cardiac signal processing using improved multivariate partial compensated transfer entropy based on non-uniform embedding," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    4. Li, Yuxing & Wu, Junxian & Yi, Yingmin & Gu, Yunpeng, 2023. "Unequal-step multiscale integrated mapping dispersion Lempel-Ziv complexity: A novel complexity metric for signal analysis," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    5. Li, Sange & Shang, Pengjian, 2022. "A new complexity measure: Modified discrete generalized past entropy based on grain exponent," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xu, Meng & Shang, Pengjian & Zhang, Sheng, 2021. "Multiscale Rényi cumulative residual distribution entropy: Reliability analysis of financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    2. Gonçalves, Bruna Amin & Carpi, Laura & Rosso, Osvaldo A. & Ravetti, Martín G., 2016. "Time series characterization via horizontal visibility graph and Information Theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 93-102.
    3. Spichak, David & Kupetsky, Audrey & Aragoneses, Andrés, 2021. "Characterizing complexity of non-invertible chaotic maps in the Shannon–Fisher information plane with ordinal patterns," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    4. Araújo, Felipe & Bastos, Lucas & Medeiros, Iago & Rosso, Osvaldo A. & Aquino, Andre L.L. & Rosário, Denis & Cerqueira, Eduardo, 2023. "Characterization of human mobility based on Information Theory quantifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    5. Borges, João B. & Ramos, Heitor S. & Mini, Raquel A.F. & Rosso, Osvaldo A. & Frery, Alejandro C. & Loureiro, Antonio A.F., 2019. "Learning and distinguishing time series dynamics via ordinal patterns transition graphs," Applied Mathematics and Computation, Elsevier, vol. 362(C), pages 1-1.
    6. Pinar Deniz Tosun & Derk-Jan Dijk & Raphaelle Winsky-Sommerer & Daniel Abasolo, 2019. "Effects of Ageing and Sex on Complexity in the Human Sleep EEG: A Comparison of Three Symbolic Dynamic Analysis Methods," Complexity, Hindawi, vol. 2019, pages 1-12, January.
    7. Amadio, Ariel & Rey, Andrea & Legnani, Walter & Blesa, Manuel García & Bonini, Cristian & Otero, Dino, 2023. "Mathematical and informational tools for classifying blood glucose signals - a pilot study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    8. Mateos, Diego M. & Zozor, Steeve & Olivares, Felipe, 2020. "Contrasting stochasticity with chaos in a permutation Lempel–Ziv complexity — Shannon entropy plane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    9. Dai, Yimei & Zhang, Hesheng & Mao, Xuegeng & Shang, Pengjian, 2018. "Complexity–entropy causality plane based on power spectral entropy for complex time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 501-514.
    10. Mateos, Diego M. & Gómez-Ramírez, Jaime & Rosso, Osvaldo A., 2021. "Using time causal quantifiers to characterize sleep stages," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    11. Traversaro, Francisco & Ciarrocchi, Nicolás & Cattaneo, Florencia Pollo & Redelico, Francisco, 2019. "Comparing different approaches to compute Permutation Entropy with coarse time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 635-643.
    12. Šapina, Matej & Karmakar, Chandan Kumar & Kramarić, Karolina & Kośmider, Marcin & Garcin, Matthieu & Brdarić, Dario & Milas, Krešimir & Yearwood, John, 2021. "Lempel-Ziv complexity of the pNNx statistics – an application to neonatal stress," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    13. Eduarda T. C. Chagas & Marcelo Queiroz‐Oliveira & Osvaldo A. Rosso & Heitor S. Ramos & Cristopher G. S. Freitas & Alejandro C. Frery, 2022. "White Noise Test from Ordinal Patterns in the Entropy–Complexity Plane," International Statistical Review, International Statistical Institute, vol. 90(2), pages 374-396, August.
    14. Zhang, Xuguang & Shu, Xiaohu & He, Zhen, 2019. "Crowd panic state detection using entropy of the distribution of enthalpy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 935-945.
    15. Benedetto, Francesco & Mastroeni, Loretta & Quaresima, Greta & Vellucci, Pierluigi, 2020. "Does OVX affect WTI and Brent oil spot variance? Evidence from an entropy analysis," Energy Economics, Elsevier, vol. 89(C).
    16. Santos, Yan Antonino Costa & Rêgo, Leandro Chaves & Ospina, Raydonal, 2022. "Online handwritten signature verification via network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    17. Traversaro, Francisco & Redelico, Francisco O., 2018. "Characterization of autoregressive processes using entropic quantifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 13-23.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:137:y:2020:i:c:s096007792030268x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.