A Markov Chain Grey Forecasting Model: A Case Study of Gasoline Demand of Iran

The objective of this paper is to evaluate two forecasting methods of gray model (GM) and Markov chain grey model (MCGM) and compare them with regression analysis. To achieve this aim, we develop a prediction model of gasoline demand in Iran. Then, the results of gray model (GM), Markov chain grey model (MCGM) and regression forecasting model are compared. The comparison reveals that the MCGM forecasting model has higher precision than GM forecasting model and regression forecasting model. The MCGM forecasting model is then used to forecast the annual gasoline demand of Iran up to the year 2020. The results provide scientific basis for the planned development of the gasoline supply in Iran.

کلیدواژه ها:

Markov chain grey model (MCGM) ، Grey model (GM) ، Forecasting ، Gasoline forecasting