Evolving granular neural network for fuzzy time series forecasting

Abstract

A primary requirement of a broad class of evolving intelligent systems is to process a sequence of numeric data over time. This paper introduces a granular neural network framework for evolving fuzzy system modeling from fuzzy data streams. The evolving granular neural network (eGNN) efficiently handles concept changes, distinctive events of nonstationary environments. eGNN constructs interpretable multi-sized local models using fuzzy neural information fusion. An incremental learning algorithm builds the neural network topology from the information contained in data streams. Here we emphasize fuzzy intervals and objects with trapezoidal membership functions. Triangular fuzzy numbers, intervals, and numeric data are particular instances of trapezoids. An example concerning weather time series forecasting illustrates the neural network performance. The goal is to extract, from monthly temperature data, information of interest to attain accurate one-step forecasts and better rapport with reality. Simulation results suggest that eGNN learns from fuzzy data successfully and is competitive with state-of-the-art approaches.