comparison of arima, fuzzy regression and fuzzy auto regressive integrated moving average methods in price forecasting

abstract nowadays, due to the environmental uncertainty and rapid development of new technologies, economic variables are often predicted by using less data and short-term timeframes. therefore, prediction methods which require fewer amounts of data are needed. auto regressive integrated moving average (arima) model and artificial neural networks (anns) need large amounts of data to achieve accurate results, however fuzzy regression (fr) models, compared with other models, are more suitable for conditions with less attainable data. in order to solve the above mentioned problem and to achieve more accurate results, in the present paper three methods have been evaluated: auto regressive integrated moving average (arima), fuzzy regression (fr), and fuzzy auto regressive integrated moving average (farima) which is resulted by combining arima and fuzzy methods. comparing the accuracy of predictions, based on two criteria rmse and r2, indicated that fuzzy auto regressive integrated moving average (farima) had the best results in forecasting the price index.