). Recently, in Ediger et al. (in press), we developed a decision support system for forecasting fossil fuel production by applying regression, Autoregressive Inte¬grated Moving Average (ARIMA), and Seasonal Auto-regressive Integrated Moving Average (SARIMA) methods to the historical data from 1950 to 2003 in a comparative manner. The method proposed in that study integrated the models obtained from each method by using some decision parameters related to goodness-of-fit or confidence inter¬val, behavior of the curve, and reserves.