IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v301y2001i1p567-588.html
   My bibliography  Save this article

A complexity score derived from principal components analysis of nonlinear order measures

Author

Listed:
  • Giuliani, Alessandro
  • Colafranceschi, Mauro
  • Webber, Charles L
  • Zbilut, Joseph P

Abstract

The generation of a global “complexity” score for numerical series was derived from a principal components analysis of a group of nonlinear measures of experimental as well simulated series. The concept of complexity was demonstrated to be independent from other descriptors of ordered series such as the amount of variance, the departure from normality and the relative nonstationarity; and to be mainly related to the number of independent elements (or operations) needed to synthesize the series. The possibility of having a univocal ranking of complexity for diverse series opens the way to a wider application of dynamical systems concepts in empirical sciences.

Suggested Citation

  • Giuliani, Alessandro & Colafranceschi, Mauro & Webber, Charles L & Zbilut, Joseph P, 2001. "A complexity score derived from principal components analysis of nonlinear order measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 301(1), pages 567-588.
    • Handle: RePEc:eee:phsmap:v:301:y:2001:i:1:p:567-588
    DOI: 10.1016/S0378-4371(01)00427-7
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437101004277
    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/S0378-4371(01)00427-7?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.

    Citations

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


    Cited by:

    1. Belaire-Franch, Jorge, 2004. "Testing for non-linearity in an artificial financial market: a recurrence quantification approach," Journal of Economic Behavior & Organization, Elsevier, vol. 54(4), pages 483-494, August.
    2. Kohestani, Havva & Totonkuban, Mahbubeh & Di Paola, Luisa & Todde, Virginia & Giuliani, Alessandro, 2018. "The basic principles of topology-dynamics relations in networks: An empirical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 584-594.
    3. Martin, R.R. & Montero, S. & Silva, E. & Bizzarri, M. & Cocho, G. & Mansilla, R. & Nieto-Villar, J.M., 2017. "Phase transitions in tumor growth: V what can be expected from cancer glycolytic oscillations?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 762-771.
    4. Alessandro Giuliani & Alessandro Vici, 2024. "On the (Apparently) Paradoxical Role of Noise in the Recognition of Signal Character of Minor Principal Components," Stats, MDPI, vol. 7(1), pages 1-11, January.
    5. Dünki, R.M. & Dressel, M., 2006. "Statistics of biophysical signal characteristics and state specificity of the human EEG," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 632-650.
    6. Baggio, Rodolfo, 2015. "Looking into the future of complex dynamic systems," MPRA Paper 65549, University Library of Munich, Germany.

    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:301:y:2001:i:1:p:567-588. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.