In this study classical and fuzzy time series methods are employed in order to forecast crude oil price. Ultimately by checking out the error standard value, fuzzy time series determined as an appropriate model. It can be concluded that in the condition of few attainable data and uncertain situations, fuzzy time series model is able to obtain acceptable results for researchers (in comparison with ARIMA which needs more than 100 data to forecast). And it can combine with classical time series to reduce their constraint
Ghamari E, Shahabi Tabari M. A Comparison of Forecasting Power in Statistical Classic Time Series’ ARIMA’ and Fuzzy Time Series (Case Study of Crude Oil Price). TFI 2014; 2 (1) :29-44 URL: http://tfe.raja.ac.ir/article-1-31-en.html
