Evolving fuzzy granular modeling from nonstationary fuzzy data streams

Abstract

Evolving granular modeling is an approach that considers online granular data stream processing and structurally adaptive rule-based models. As uncertain data prevail in stream applications, excessive data granularity becomes unnecessary and inefficient. This paper introduces an evolving fuzzy granular framework to learn from and model time-varying fuzzy input–output data streams. The fuzzy-set based evolving modeling framework consists of a one-pass learning algorithm capable to gradually develop the structure of rule-based models. This framework is particularly suitable to handle potentially unbounded fuzzy data streams and render singular and granular approximations of nonstationary functions. The main objective of this paper is to shed light into the role of evolving fuzzy granular computing in providing high-quality approximate solutions from large volumes of real-world online data streams. An application example in weather temperature prediction using actual data is used to evaluate and illustrate the usefulness of the modeling approach. The behavior of nonstationary fuzzy data streams with gradual and abrupt regime shifts is also verified in the realm of the weather temperature prediction.