Abstract
Evolving participatory learning (ePL) modeling joins the concepts of participatory learning and evolving fuzzy systems. It uses data streams to continuously adapt the structure and functionality of fuzzy models. This paper suggests an enhanced version of the ePL approach, called ePL+, which includes both an utility measure to shrink rule bases, and a variable cluster radius mechanism to improve the cluster structure. These features are useful in adaptive fuzzy rule-based modeling to recursively construct local fuzzy models with variable zone of influence. Moreover, ePL+ extends ePL to multi-input, multi-output fuzzy system modeling. Computational experiments considering financial returns volatility modeling and forecasting are conducted to compare the performance of the ePL+ approach with state of the art fuzzy modeling methods and with GARCH modeling. The experiments use actual data of S&P 500 and Ibovespa stock market indexes. The results suggest that the ePL+ approach is highly capable to model volatility dynamics, in a robust, flexible, parcimonious, and autonomous way.
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