DEVELOPING COMBINED FORECASTING MODELS IN OIL INDUSTRY A Case Study in Opec Oil Demand

The purpose of this research is to study the combined forecasting methods in energy section. This method is a new approach which leads to considerable reduction of error in forecasting results. In this study, forecasting has been done through using individual methods (these methods consist of exponential smoothing methods, trend analysis, box-Jenkins, causal analysis, and neural network models) and also combining methods. In next step, the Results of these individual forecasting methods have been combined and compared with artificial neural networks, and multiple regression models. The data we used in this study are: dependent variable: OPEC oil demands from 1960 to 2005, and independent variables: oil price, GDP, other energy demands, population, and added-value in industry (in OECD countries. Computed indexes of errors are: MSE, MAPE, and GAPE which show considerable reductions in the errors of forecasting when using combining models. Therefore, it is suggested that the designed models could be applied for oil demand forecasting.