k-NN Based Neuro-fuzzy System for Time Series Prediction

Neuro-fuzzy systems have been proposed for different applications for many years. In this paper, a k-NN based neuro-fuzzy predictor is developed for time series prediction. We use a neuro-fuzzy system to generate prediction results. A set of fuzzy rules can be generated by a self-constructing clustering method. These rules can be refined by a hybrid learning algorithm. In stead of using all training data to training a model, we utilize the k-NN method to dynamically select k instances for each prediction. Experimental results show that our approach can provide more accurate predictions than other methods.