m Khashei - Isfahan University Of Technology
f Mokhatab Rafiei - Isfahan University Of Technology
m Bijari

In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models arefundamental to make decision and hence the research for improving the effectiveness of forecasting models have beencurried on. Many researchers have compared different timeseries models together in order to determine more efficient once in financial markets. In this paper, the performance offour interval time series models including autoregressive integrated moving average (ARIMA), fuzzy autoregressiveintegrated moving average (FARIMA), hybrid ANNs and fuzzy (F ANN) and Improved F ARIMA models are compared together. Empirical results of exchange rate forecastingindicate that the F ANN model is more satisfactory than other those models. Therefore, it can be a suitable alternativemodel for interval forecasting of financial time series

Artificial Neural Networks (ANNs); AutoRegressive Integrated Moving Average (ARIMA); Time series forecasting; Hybrid forecasts; Interval models; Exchange rate