IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v362y2019ic20.html
   My bibliography  Save this article

Learning and distinguishing time series dynamics via ordinal patterns transition graphs

Author

Listed:
  • Borges, João B.
  • Ramos, Heitor S.
  • Mini, Raquel A.F.
  • Rosso, Osvaldo A.
  • Frery, Alejandro C.
  • Loureiro, Antonio A.F.

Abstract

Strategies based on the extraction of measures from ordinal patterns transformation, such as probability distributions and transition graphs, have reached relevant advancements in distinguishing different time series dynamics. However, the reliability of such measures depends on the appropriate selection of parameters and the need for large time series. In this paper we present a method for the characterization of distinct time series behaviors based on the probability of self-transitions, a measure extracted from their transformation onto ordinal patterns transition graphs. We validate our method by investigating the main characteristics of periodic, random, and chaotic time series. By the application of learning strategies, we precisely classify different randomness levels in time series, reaching 100% in accuracy, and advances in performing the hard task of distinguishing random noises from chaotic time series, correctly distinguishing 96.61% of the cases. Furthermore, we show that this strategy is well suitable to be used by many applications, even for short time series, and does not depend on the selection of parameters.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:apmaco:v:362:y:2019:i:c:20
    DOI: 10.1016/j.amc.2019.06.068
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300319305375
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2019.06.068?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. 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.
    2. Martín Gómez Ravetti & Laura C Carpi & Bruna Amin Gonçalves & Alejandro C Frery & Osvaldo A Rosso, 2014. "Distinguishing Noise from Chaos: Objective versus Subjective Criteria Using Horizontal Visibility Graph," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-15, September.
    3. Osvaldo A Rosso & Raydonal Ospina & Alejandro C Frery, 2016. "Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-19, December.
    4. Andre L.L. Aquino & Tamer S.G. Cavalcante & Eliana S. Almeida & Alejandro C. Frery & Osvaldo A. Rosso, 2015. "Characterization of vehicle behavior with information theory," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(10), pages 1-12, October.
    5. Rosso, Osvaldo A. & Carpi, Laura C. & Saco, Patricia M. & Gómez Ravetti, Martín & Plastino, Angelo & Larrondo, Hilda A., 2012. "Causality and the entropy–complexity plane: Robustness and missing ordinal patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 42-55.
    6. Lamberti, P.W & Martin, M.T & Plastino, A & Rosso, O.A, 2004. "Intensive entropic non-triviality measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 334(1), pages 119-131.
    7. Tang, Jinjun & Wang, Yinhai & Liu, Fang, 2013. "Characterizing traffic time series based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4192-4201.
    8. 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.
    9. Aquino, Andre L.L. & Ramos, Heitor S. & Frery, Alejandro C. & Viana, Leonardo P. & Cavalcante, Tamer S.G. & Rosso, Osvaldo A., 2017. "Characterization of electric load with Information Theory quantifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 277-284.
    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. 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).
    2. Yair Neuman & Yochai Cohen, 2022. "Predicting Change in Emotion through Ordinal Patterns and Simple Symbolic Expressions," Mathematics, MDPI, vol. 10(13), pages 1-18, June.
    3. Wang, Xiaoyan & Tang, Ming & Guan, Shuguang & Zou, Yong, 2023. "Quantifying time series complexity by multi-scale transition network approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(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. 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.
    2. 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).
    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. 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).
    5. 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.
    6. Montani, Fernando & Deleglise, Emilia B. & Rosso, Osvaldo A., 2014. "Efficiency characterization of a large neuronal network: A causal information approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 58-70.
    7. Traversaro, Francisco & Legnani, Walter & Redelico, Francisco O., 2020. "Influence of the signal to noise ratio for the estimation of Permutation Entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    8. Baggio, Rodolfo & Sainaghi, Ruggero, 2016. "Mapping time series into networks as a tool to assess the complex dynamics of tourism systems," Tourism Management, Elsevier, vol. 54(C), pages 23-33.
    9. Wang, Zhuo & Shang, Pengjian, 2021. "Generalized entropy plane based on multiscale weighted multivariate dispersion entropy for financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    10. 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.
    11. Di Vece, Marzio & Garlaschelli, Diego & Squartini, Tiziano, 2023. "Reconciling econometrics with continuous maximum-entropy network models," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    12. Baravalle, Roman & Rosso, Osvaldo A. & Montani, Fernando, 2017. "A path integral approach to the Hodgkin–Huxley model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 986-999.
    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. 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.
    15. Juan G Colonna & José R H Carvalho & Osvaldo A Rosso, 2020. "Estimating ecoacoustic activity in the Amazon rainforest through Information Theory quantifiers," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-21, July.
    16. Aquino, Andre L.L. & Ramos, Heitor S. & Frery, Alejandro C. & Viana, Leonardo P. & Cavalcante, Tamer S.G. & Rosso, Osvaldo A., 2017. "Characterization of electric load with Information Theory quantifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 277-284.
    17. 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).
    18. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    19. Fernandes, Leonardo H.S. & de Araujo, Fernando H.A. & Tabak, Benjamin M., 2021. "Insights from the (in)efficiency of Chinese sectoral indices during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    20. Aurelio F. Bariviera & Luciano Zunino & Osvaldo A. Rosso, 2016. "Crude Oil Market And Geopolitical Events: An Analysis Based On Information-Theory-Based Quantifiers," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 21(1), pages 41-51, May.

    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:apmaco:v:362:y:2019:i:c:20. 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: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.