An adaptive ordered fuzzy time series with application to FOREX |
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Authors: | Majid Bahrepour Mohammad-R. Akbarzadeh-T. Mahdi Yaghoobi Mohammad-B. Naghibi-S. |
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Affiliation: | 1. Linz Institute of Technology (LIT) and Department of Applied Statistics, Johannes Kepler University in Linz, Austria;2. Department of Statistics, University of Valparaíso, Valparaíso, Chile;3. Institute of Mathematics, Faculty of Science, P.J. ?afárik University in Ko?ice, Slovakia;4. Faculty of Commerce, Chuo University, Hachioji, Tokyo 192-0393, Japan;5. Faculty of Business Administration, Toyo University, Hakusan, Tokyo 112–8606, Japan;6. Department of Mathematical Analysis and Numerical Mathematics, Comenius University, Bratislava, Slovakia;7. Facultad de Administracióny Negocios, Universidad Autónoma de Chile, Ingeniería Comercial, Santiago, Chile;8. School of Mathematics & Statistical Sciences, Arizona State University, Tempe, AZ,USA |
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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. |
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