In this research, combined forecasting is considered. This model is a new
approach that is used in two recent decade and indicate considerable
reduction of error in forecasted numbers. In this study, at first, forecasting was
done with some different methods that named individual methods.
These models consist of exponential smoothing methods, trend analysis,
box-jenkins, causal analysis and neural network model. Results of these
individual forecasting methods (some selected model) are combined and
comparaed with artificial neural network and multiple regression models.
Used data consist of Opec oil demand from 1960 to 2002 as dependent
variable and price, GDP, other energy demend, population, added value in
industry as independent variables. In mono-variable methods only dependent
variable is entered. Data of 1960-1996 are used for all variables and testing.
data is put under observation between 1996 to 2002. Computed MSE, MAPE,
GAPE indexes is shown considerable reduction in errors of forecasting.