The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

The Autoregressive Integrated Moving Average model (ARIMA) is a popular time-series model used to predict future trends in economics, energy markets, and stock markets. It has not been widely applied to enrollment forecasting in higher education. The accuracy of the ARIMA model heavily relies on the length of time series. Researchers and practitioners often utilize the most recent - to -years of historical data to predict future enrollment; however, the accuracy of enrollment projection under different lengths of time series has never been investigated and compared. A simulation and an empirical study were conducted to thoroughly investigate the accuracy of ARIMA forecasting under four different lengths of time series. When the ARIMA model completely captured the historical changing trajectories, it provided the most accurate predictions of student enrollment with 20-years of historical data and had the lowest forecasting accuracy with the shortest time series. The results of this paper contribute as a reference to studies in the enrollment projection and time-series forecasting. It provides a practical impact on enrollment strategies, budges plans, and financial aid policies at colleges and institutions across countries.

Undergraduate International Student Enrollment Forecasting Model: An Application of Time Series Analysis

This study developed statistical models to forecast international undergraduate student enrollment at a Midwest university. The authors constructed a SARIMA (Seasonal Autoregressive Integrated Moving Average) model with input variables to estimate future enrollment. The SARIMA model reflected enrollment patterns by semester through highlighting seasonality. Further, authors added input variables such as visa policy changes, the rapid increase of Chinese undergraduate enrollment, and tuition rate into the model estimation. The visa policy change and the increase of Chinese undergraduate enrollment were significant predictors of international undergraduate enrollment. The effect of tuition rates was significant but minimal in magnitude. Findings of this study generate significant implications for policy, enrollment management, and student services for international students.