IDEAS home Printed from https://ideas.repec.org/a/ids/injams/v3y2011i2p121-142.html
   My bibliography  Save this article

Synergy of chaos theory and artificial neural networks in chaotic time series forecasting

Author

Listed:
  • Muhammad Ardalani-Farsa
  • Saeed Zolfaghari

Abstract

A unique technique based on chaos theory and artificial neural networks (ANN) is developed to analyse and forecast chaotic time series. An embedding theorem is used to determine the embedding parameters. Accordingly the chaotic time series is reconstructed into phase space points. Based on chaos theory, there exists an unknown mathematical equation which can forecast the future value of the phase space points. Therefore, the embedded phase space points are fed into a neural network and trained. When the unknown phase space is predicted, the future value of time series is obtained accordingly. Two neural network architectures, feedforward and Elman, are utilised in this study. The Mackey-Glass (M-G), logistic and Henon time series are used to validate the performance of the proposed technique. The numerical experimental results confirm that the proposed method can forecast the chaotic time series effectively and accurately when compared with the existing forecasting methods.

Suggested Citation

  • Muhammad Ardalani-Farsa & Saeed Zolfaghari, 2011. "Synergy of chaos theory and artificial neural networks in chaotic time series forecasting," International Journal of Applied Management Science, Inderscience Enterprises Ltd, vol. 3(2), pages 121-142.
  • Handle: RePEc:ids:injams:v:3:y:2011:i:2:p:121-142
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=40230
    Download Restriction: Access to full text is restricted to subscribers.

    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. Halkos, George & Salamouris, Dimitrios, 2001. "Efficiency Measures of the Greek Banking Sector: A Non-Parametric Approach for the Period 1997-1999," MPRA Paper 2858, University Library of Munich, Germany.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    Full references (including those not matched with items on IDEAS)

    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:ids:injams:v:3:y:2011:i:2:p:121-142. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Darren Simpson). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=286 .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.