AN EXTENDED FUZZY ARTIFICIAL NEURAL NETWORKS MODEL FOR TIME SERIES FORECASTING

Improving time series forecasting accuracy is an important yet often difficult task. Both theoretical and empirical findings have indicated that integration of several models is an effective way to improve predictive performance, especially when the models in combination are quite different. In this paper, a model of the hybrid artificial neural networks and fuzzy model is proposed for time series forecasting, using autoregressive integrated moving average models. In the proposed model, by first modeling the linear components, autoregressive integrated moving average models are combined with the these hybrid models to yield a more general and accurate forecasting model than the traditional hybrid artificial neural networks and fuzzy models. Empirical results for financial time series forecasting indicate that the proposed model exhibits effectively improved forecasting accuracy and hence is an appropriate forecasting tool for financial time series forecasting.