For successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one's actions. In this paper, we propose to use probabilistic fuzzy systems for this purpose. We concentrate on Takagi-Sugeno (TS) probabilistic fuzzy systems that combine interpretability of fuzzy systems with the statistical properties of probabilistic systems. We start by recapitulating the general architecture of TS probabilistic fuzzy rule-based systems and summarize the corresponding reasoning schemes. We mention how probabilities can be estimated from a given data set and how a probability distribution can be approximated using a fuzzy histogram technique. We apply our methodology to financial time series analysis and demonstrate how a probabilistic TS fuzzy system can be identified, assuming that a linguistic term set is given. We illustrate the interpretability of such a system by inspecting the rule bases of our induced models.

Data-driven design, Fuzzy reasoning, Fuzzy rule base, Probabilistic fuzzy systems, Time series analysis
dx.doi.org/10.1016/j.ijar.2003.08.007, hdl.handle.net/1765/65330
International Journal of Approximate Reasoning
Erasmus Research Institute of Management

van den Berg, J.H, Kaymak, U, & van den Bergh, W.-M. (2004). Financial markets analysis by using a probabilistic fuzzy modelling approach. In International Journal of Approximate Reasoning (Vol. 35, pp. 291–305). doi:10.1016/j.ijar.2003.08.007