An adaptive ordered fuzzy time series with application to FOREX

  • Authors:

  • Majid Bahrepour;Mohammad-R. Akbarzadeh-T.;Mahdi Yaghoobi;Mohammad-B. Naghibi-S.

  • Affiliations:

  • Islamic Azad University, Mashhad Branch, Iran;Center for Applied Research on Intelligent Systems and Soft Computing, Departments of Electrical Engineering and Computer Engineering, Ferdowsi University of Mashhad, Iran;Islamic Azad University, Mashhad Branch, Iran;Department of Electrical Engineering, Ferdowsi University of Mashhad, Iran

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

An adaptive ordered fuzzy time series is proposed that employs an adaptive order selection algorithm for composing the rule structure and partitions the universe of discourse into unequal intervals based on a fast self-organising strategy. The automatic order selection of FTS as well as the adaptive partitioning of each interval in the universe of discourse is shown to greatly affect forecasting accuracy. This strategy is then applied to prediction of FOREX market. Financial markets, such as FOREX, are generally attractive applications of FTS due to their poorly understood model as well as their great deal of uncertainty in terms of quote fluctuations and the behaviours of the humans in the loop. Specifically, since the FOREX market can exhibit different behaviours at different times, the adaptive order selection is executed online to find the best order of the FTS for current prediction. The order selection module uses voting, statistical analytic and emotional decision making agents. Comparison of the proposed method with earlier studies demonstrates improved prediction accuracy at similar computation cost.