IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v513y2019icp635-643.html
   My bibliography  Save this article

Comparing different approaches to compute Permutation Entropy with coarse time series

Author

Listed:
  • Traversaro, Francisco
  • Ciarrocchi, Nicolás
  • Cattaneo, Florencia Pollo
  • Redelico, Francisco

Abstract

Bandt and Pompe introduced Permutation Entropy as a complexity measure and has been widely used in time series analysis and in many fields of nonlinear dynamics. In theory these time series come from a process that generates continuous values, and if equal values exists in a neighborhood, xt∗=xt,t∗≠t, they can be neglected with no consequences because their probability of occurrence is insignificant. Since then, this measure has been modified and extended, in particular in cases when the amount of equal values in the time series is large due to the observational method, and cannot be neglected. We test the new Data Driven Method of Imputation that cope with this type of time series without modifying the essence of the Bandt and Pompe Probability Distribution Function and compare it with the Modified Permutation Entropy, a complexity measure that assumes that equal values are not from artifacts of observations but they are typical of the data generator process. The Data Driven Method of Imputation proves to outperform the Modified Permutation Entropy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:513:y:2019:i:c:p:635-643
    DOI: 10.1016/j.physa.2018.08.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118309518
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.08.021?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. 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.
    2. Matilla-García, Mariano & Ruiz Marín, Manuel, 2009. "Detection of non-linear structure in time series," Economics Letters, Elsevier, vol. 105(1), pages 1-6, October.
    3. Redelico, Francisco O. & Traversaro, Francisco & Oyarzabal, Nicolás & Vilaboa, Ivan & Rosso, Osvaldo A., 2017. "Evaluation of the status of rotary machines by time causal Information Theory quantifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 321-329.
    4. Carpi, Laura C. & Saco, Patricia M. & Rosso, O.A., 2010. "Missing ordinal patterns in correlated noises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(10), pages 2020-2029.
    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. Chen, Yu & Ling, Guang & Song, Xiangxiang & Tu, Wenhui, 2023. "Characterizing the statistical complexity of nonlinear time series via ordinal pattern transition networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    2. Lu, Peng & Ye, Lin & Pei, Ming & Zhao, Yongning & Dai, Binhua & Li, Zhuo, 2022. "Short-term wind power forecasting based on meteorological feature extraction and optimization strategy," Renewable Energy, Elsevier, vol. 184(C), pages 642-661.
    3. Zhao, Xiaojun & Ji, Mengfan & Zhang, Na & Shang, Pengjian, 2020. "Permutation transition entropy: Measuring the dynamical complexity of financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Lu, Peng & Ye, Lin & Tang, Yong & Zhao, Yongning & Zhong, Wuzhi & Qu, Ying & Zhai, Bingxu, 2021. "Ultra-short-term combined prediction approach based on kernel function switch mechanism," Renewable Energy, Elsevier, vol. 164(C), pages 842-866.

    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. Miguel Henry & George Judge, 2019. "Permutation Entropy and Information Recovery in Nonlinear Dynamic Economic Time Series," Econometrics, MDPI, vol. 7(1), pages 1-16, March.
    2. Herrera Gómez, Marcos & Ruiz Marín, Manuel & Mur Lacambra, Jesús, 2014. "Testing Spatial Causality in Cross-section Data," MPRA Paper 56678, University Library of Munich, Germany.
    3. Bariviera, Aurelio F. & Font-Ferrer, Alejandro & Sorrosal-Forradellas, M. Teresa & Rosso, Osvaldo A., 2019. "An information theory perspective on the informational efficiency of gold price," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    4. Weiß, Christian H. & Ruiz Marín, Manuel & Keller, Karsten & Matilla-García, Mariano, 2022. "Non-parametric analysis of serial dependence in time series using ordinal patterns," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    5. 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).
    6. Yongmiao Hong & Xia Wang & Wenjie Zhang & Shouyang Wang, 2017. "An efficient integrated nonparametric entropy estimator of serial dependence," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 728-780, October.
    7. 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).
    8. Saco, Patricia M. & Carpi, Laura C. & Figliola, Alejandra & Serrano, Eduardo & Rosso, Osvaldo A., 2010. "Entropy analysis of the dynamics of El Niño/Southern Oscillation during the Holocene," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 5022-5027.
    9. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2010. "Complexity-entropy causality plane: A useful approach to quantify the stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1891-1901.
    10. repec:onb:oenbwp:y::i:165:b:1 is not listed on IDEAS
    11. 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).
    12. Marcos Herrera & Jesús Mur & Manuel Ruiz, 2016. "Detecting causal relationships between spatial processes," Papers in Regional Science, Wiley Blackwell, vol. 95(3), pages 577-594, August.
    13. 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).
    14. 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.
    15. 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).
    16. Helmut Elsinger, 2010. "Independence Tests based on Symbolic Dynamics," Working Papers 165, Oesterreichische Nationalbank (Austrian Central Bank).
    17. Marcos Herrera & Manuel Ruiz & Jesús Mur, 2013. "Detecting Dependence Between Spatial Processes," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(4), pages 469-497, February.
    18. Aurelio F. Bariviera & Angelo Plastino & George Judge, 2018. "Spurious Seasonality Detection: A Non-Parametric Test Proposal," Econometrics, MDPI, vol. 6(1), pages 1-15, January.
    19. Liu, Hongzhi & Zhang, Xingchen & Zhang, Xie, 2020. "Multiscale complexity analysis on airport air traffic flow volume time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    20. 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).
    21. Yoon, Gawon, 2010. "Do real exchange rates really follow threshold autoregressive or exponential smooth transition autoregressive models?," Economic Modelling, Elsevier, vol. 27(2), pages 605-612, March.

    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:phsmap:v:513:y:2019:i:c:p:635-643. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.