The Extreme Value Evolving Predictor

This article introduces a new evolving fuzzy-rule-based algorithm for online data streams, named extreme value evolving predictor (EVeP). It offers a statistically well-founded approach to define the evolving fuzzy granules that form the antecedent and the consequent parts of the rules. The evolving fuzzy granules correspond to radial inclusion Weibull functions. They are interpreted by the extreme value theory as the limiting distribution of the relative proximity among the rules of the learning model. Regarding the parameters of the Takagi-Sugeno term at the consequent of the rules, the algorithm enhances the already demonstrated benefits of multitask learning by replacing a binary version with a fuzzy structural relationship among the rules. The pairwise similarity among the rules is automatically provided by the current interaction of the evolving fuzzy granules at the antecedent and at the consequent parts of their corresponding rules. Several computational experiments, using artificial and real-world time series, attest to the dominating prediction performance of EVeP when compared to the state-of-the-art evolving algorithms.

Automated summary

This article introduces a new evolving fuzzy-rule-based algorithm named extreme value evolving predictor (EVeP), which offers a statistically well-founded approach to improve the prediction performance of rules by using evolving fuzzy granules and fuzzy structural relationships. It outperforms the state-of-the-art evolving algorithms in terms of prediction performance.