Comparison of Hybrid Intelligent Systems, Neural Networks and Interval Type-2 Fuzzy Logic for Time Series Prediction

Uncertainty is an inherent part of intelligent systems used in real-world applications. The use of new methods for handling incomplete information is of fundamental importance. Type-1 fuzzy sets used in conventional fuzzy systems cannot fully handle the uncertainties present in intelligent systems. Type-2 fuzzy sets can handle such uncertainties in a better way because they provide us with a more complete model of real-world uncertainty. Experimental results are also presented for forecasting chaotic time series in which interval type-2 fuzzy logic outperforms some hybrid intelligent approaches. Neural networks provide a comparable result with type-2 fuzzy systems.