Fuzzy granular evolving modeling for time series prediction

Modeling large volumes of flowing data from complex systems motivates rethinking several aspects of the machine learning theory. Data stream mining is concerned with extracting structured knowledge from spatio-temporally correlated data. A profusion of systems and algorithms devoted to this end has been constructed under the conceptual framework of granular computing. This paper outlines a fuzzy set based granular evolving modeling — FBeM — approach for learning from imprecise data. Granulation arises because modeling uncertain data dispenses attention to details. The evolving aspect is fundamental to account endless flows of nonstationary data and structural adaptation of models. Experiments with classic Box-Jenkins and Mackey-Glass benchmarks as well as with actual Global40 bond data suggest that the FBeM approach outperforms alternative approaches.