English

Abstract

In current years, a variety of time series models have been planned for financial markets forecasting. In each case, the exactness of time series forecasting models are primary to make decision and hence the research for humanizing the efficiency of forecasting models have been curried on. Lots of researchers have compared diverse time series models mutually in order to establish more proficient once in financial markets. In this paper, the performance of four interval time series models including autoregressive integrated moving average (ARIMA), fuzzy autoregressive integrated moving average (FARIMA), hybrid ANNs and fuzzy (FANN) and Improved FARIMA models are compared together. Empirical results of exchange rate forecasting indicate that the FANN model is more acceptable than other those models. Consequently, it can be a suitable alternative model for interval forecasting of financial time series.