Proposing a synthetic approach (FARIMA) by employing ARIMA and fuzzy regression methods in order to forecast crude oil price
Subject Areas : Applied Economics
قدرت الله
امام وردی
1
(ندارد)
مریم
شهابی طبری
2
(مسئول مکاتبات)
Keywords: Auto Regressive Integrated Mov, Fuzzy Auto Regressive Integrat, Forecasting, World crude Oil Price,
Abstract :
The ARIMA model is a precise forecasting model for short time periods, but the limitation of a large amount of historical data is required. However, in our society, due to uncertainty and rapid development of new technology, we usually have to forecast future situations using little data in a short span of time. The historical data must be less than what the ARIMA model employs which limits its application. The fuzzy regression is able to forecast model which is suitable for the uncertain condition and with little attainable historical data. But the results of this model cannot be encouraging because the spread is wide in some cases. The researchers do try to combine the advantages of the fuzzy regression and ARIMA models to formulate the FARIMA model and to overcome the limitations of the fuzzy regression and ARIMA model. Therefore, in this study, a synthetic fuzzy auto regressive integrated moving average (FARIMA) is employed to forecast crude oil price. The findings show that the proposed method can get more satisfactory results.
