IDEAS home Printed from
   My bibliography  Save this article

Fuzzy Autoregressive Rules: Towards Linguistic Time Series Modeling


  • Jose Luis Aznarte
  • Jesus Alcala-Fdez
  • Antonio Arauzo
  • Jose Manuel Benitez


Fuzzy rule-based models, a key element in soft computing (SC), have arisen as an alternative for time series analysis and modeling. One difference with preexisting models is their interpretability in terms of human language. Their interactions with other components have also contributed to a huge development in their identification and estimation procedures. In this article, we present fuzzy rule-based models, their links with some regime-switching autoregressive models, and how the use of soft computing concepts can help the practitioner to solve and gain a deeper insight into a given problem. An example on a realized volatility series is presented to show the forecasting abilities of a fuzzy rule-based model.

Suggested Citation

  • Jose Luis Aznarte & Jesus Alcala-Fdez & Antonio Arauzo & Jose Manuel Benitez, 2011. "Fuzzy Autoregressive Rules: Towards Linguistic Time Series Modeling," Econometric Reviews, Taylor & Francis Journals, vol. 30(6), pages 646-668.
  • Handle: RePEc:taf:emetrv:v:30:y:2011:i:6:p:646-668
    DOI: 10.1080/07474938.2011.553569

    Download full text from publisher

    File URL:
    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.


    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:taf:emetrv:v:30:y:2011:i:6:p:646-668. 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: (). General contact details of provider: .

    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.