Enrollment forecasting is the central component of effective budget and program planning. The integrated enrollment forecast model is developed to achieve a better understanding of the variables affecting student enrollment and, ultimately, to perform accurate forecasts. The transfer function model of the autoregressive integrated moving average (ARIMA) methodology and linear regression model are major forecasting techniques. The structural approach embedded in the models allows the researcher to construct candidate models, eliminate inappropriate ones, and retain the most suitable model. In addition, the expert system for the ARIMA model is a supplementary tool used to verify the resulting models in terms of model structure and forecasting accuracy. The enrollment series of interest is the 1962-2004 student enrollment for Oklahoma State University (OSU). Fifteen independent variables are used in an attempt to increase explanatory power. These variables include demographics (Oklahoma high school graduates and competitor college enrollment from the University of Oklahoma), state funding, economic indicators, (e.g., state unemployment rate and gross national product), and one-year lagged demographics and economic indicators. The best ARIMA and linear regression models yield remarkably high R-squared values and exceptionally small mean absolute percentage errors (MAPEs), respectively. Moreover, they contain two identical demographics: Oklahoma high school graduates and one-year lagged OSU enrollment. Hence, the first-order autoregressive models appropriately depict the longitudinal and aggregated OSU enrollment series. An additional linear regression model shows that one-year lagged Oklahoma high school graduates and three economic indicators significantly contribute to OSU enrollment. This integrated enrollment forecast model has demonstrated its model validity and accuracy. Hence, it could be replicated for comparable universities elsewhere. (A bibliography is included. Contains 5 figures and 4 tables.)