IDEAS home Printed from https://ideas.repec.org/a/wly/apsmbi/v34y2018i5p633-644.html
   My bibliography  Save this article

Model‐free offline change‐point detection in multidimensional time series of arbitrary nature via ϵ‐complexity: Simulations and applications

Author

Listed:
  • Boris Darkhovsky
  • Alexandra Piryatinska

Abstract

A novel method for offline detection of multiple change points in multidimensional time series is proposed. It is based on the notion of ε‐complexity of continuous vector functions. The proposed methodology does not use any prior information on data‐generating mechanisms; therefore, it can be applied to multidimensional time series of arbitrary nature. Its performance is demonstrated in simulations and an application to high‐frequency financial data.

Suggested Citation

  • Boris Darkhovsky & Alexandra Piryatinska, 2018. "Model‐free offline change‐point detection in multidimensional time series of arbitrary nature via ϵ‐complexity: Simulations and applications," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 34(5), pages 633-644, September.
    • Handle: RePEc:wly:apsmbi:v:34:y:2018:i:5:p:633-644
    DOI: 10.1002/asmb.2303
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asmb.2303
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asmb.2303?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
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:wly:apsmbi:v:34:y:2018:i:5:p:633-644. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .

    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.