AN EXTENDED FUZZY ARTIFICIAL NEURAL NETWORKS MODEL FOR TIME SERIES FORECASTING

Improving time series forecasting accuracy is an important yet often dicult task. Both theoretical and empirical ndings have indicated that integration of several models is an eective way to improve predictive performance, especially when the models in combination are quite dierent. In this paper, a model of the hybrid articial neural networks and fuzzy model is proposed for time series forecasting, using autoregressive integrated moving average models. In the proposed model, by rst 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 articial neural networks and fuzzy models. Empirical results for nancial time series forecasting indicate that the proposed model exhibits eectively improved forecasting accuracy and hence is an appropriate forecasting tool for nancial time series forecasting.