IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v333y2004icp1-9.html
   My bibliography  Save this article

Long and short-term correlation properties of computer-generated fractional Gaussian noise

Author

Listed:
  • Aitken, George J.M

Abstract

It is well known that fractional Gaussian noise (fGn) generated by synthesizing the fGn autocorrelation structure exhibits exact short and long-term dependence, as well as a good approximation to the theoretical power-law spectrum. In contrast, the spectral synthesis method (SSM), based on synthesizing the fGn power spectrum, generates time series that have the desired spectra, but not the correct dependence. This paper shows that the autocorrelation-based algorithm, called the fractional Gaussian process algorithm, produces a random, zero-frequency spectral component that accounts for the difference in behavior. Furthermore, any algorithm that does not produce the appropriate zero-frequency term cannot generate an fGn series with correct correlation properties. It is shown that the practice of using short segments of long SSM-generated time series does not fully compensate for the absence of the zero-frequency term.

Suggested Citation

  • Aitken, George J.M, 2004. "Long and short-term correlation properties of computer-generated fractional Gaussian noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 1-9.
    • Handle: RePEc:eee:phsmap:v:333:y:2004:i:c:p:1-9
    DOI: 10.1016/j.physa.2003.10.046
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843710300935X
    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.2003.10.046?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:333:y:2004:i:c:p:1-9. 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.