IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v382y2007i1p9-15.html
   My bibliography  Save this article

Detrending moving average algorithm: A closed-form approximation of the scaling law

Author

Listed:
  • Arianos, Sergio
  • Carbone, Anna

Abstract

The Hurst exponent H of long range correlated series can be estimated by means of the detrending moving average (DMA) method. The computational tool, on which the algorithm is based, is the generalized variance σDMA2=1/(N-n)∑i=nN[y(i)-y˜n(i)]2, with y˜n(i)=1/n∑k=0ny(i-k) being the average over the moving window n and N the dimension of the stochastic series y(i). The ability to yield H relies on the property of σDMA2 to vary as n2H over a wide range of scales [E. Alessio, A. Carbone, G. Castelli, V. Frappietro, Eur. J. Phys. B 27 (2002) 197]. Here, we give a closed form proof that σDMA2 is equivalent to CHn2H and provide an explicit expression for CH. We furthermore compare the values of CH with those obtained by applying the DMA algorithm to artificial self-similar signals.

Suggested Citation

  • Arianos, Sergio & Carbone, Anna, 2007. "Detrending moving average algorithm: A closed-form approximation of the scaling law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 9-15.
  • Handle: RePEc:eee:phsmap:v:382:y:2007:i:1:p:9-15
    DOI: 10.1016/j.physa.2007.02.074
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437107001306
    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.2007.02.074?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.

    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:382:y:2007:i:1:p:9-15. 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.