Fuzzy-neural approach to time series prediction

Abstract

This paper presents a fuzzy-neural approach to prediction of nonlinear time series. The underlying mechanism governing the time series, expressed as a set of IF-THEN rules, is discovered by a modified self-organizing counterpropagation network. The task of predicting the future is carried out by a fuzzy predictor on the basis of the extracted rules. We have applied the approach to three well studied time series. Comparative studies with the other approaches on the sunspot, flour prices, and Mackey-Glass chaotic time series suggest that our approach can offer comparable or even better performances. One of the salient features of the approach is that only single leaning epoch is needed, thereby providing a useful paradigm for some situations where the fast learning is critical.