IDEAS home Printed from
   My bibliography  Save this article

A Seasonal Fuzzy Time Series Forecasting Method Based On Gustafson-Kessel Fuzzy Clustering


  • Faruk ALPASLAN

    () (University of Ondokuz Mayis, Turkey)

  • Ozge CAGCAG

    () (University of Ondokuz Mayis, Turkey)


Fuzzy time series forecasting methods do not require constraints found in conventional approaches. In addition, due to uncertainty that they contain, many time series to be forecasted should be considered as fuzzy time series. Fuzzy time series forecasting models consist of three steps as fuzzification,identification of fuzzy relations and defuzzification. Although most of the time series encountered in real life contain seasonal component, only few of these fuzzy time series approaches analyze seasonal fuzzy time series. Even though all these studies have various advantages, their biggest disadvantage is to take into consideration only the fuzzy set having the highest membership value rather than the membership value of observations belonging to each fuzzy set. This situation conflicts to fuzzy set theory and causes the loss of information thus, negatively affects on the forecasting performance. In this study, a seasonal fuzzy time series forecasting model, in which Gustafson-Kessel fuzzy clustering technique in fuzzification stage is initially used and membership values are taken into account in both the determining fuzzy relations and the defuzzification stages is proposed. The proposed method is applied to real life seasonal time series and substantial results are obtained.

Suggested Citation

  • Faruk ALPASLAN & Ozge CAGCAG, 2012. "A Seasonal Fuzzy Time Series Forecasting Method Based On Gustafson-Kessel Fuzzy Clustering," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 1(2), pages 1-13, DECEMBER.
  • Handle: RePEc:aes:jsesro:v:1:y:2012:i:2:p:1-13

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Seasonal fuzzy time series; Gustafson-Kessel fuzzy clustering; membership; value; forecasting.;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods


    Access and download statistics


    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:aes:jsesro:v:1:y:2012:i:2:p:1-13. 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: (Bogdan-Vasile Ileanu). 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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.