Student Trajectories for Enrollment Forecasting, Management, and Planning

Enrollment Forecasting based on Modified Weight Fuzzy Time Series

Journal of Artificial Intelligence ◽

10.3923/jai.2011.110.118 ◽

2010 ◽

Vol 4 (1) ◽

pp. 110-118 ◽

Cited By ~ 17

Author(s):

Z. Ismail ◽

R. Efendi

Keyword(s):

Time Series ◽

Fuzzy Time Series ◽

Enrollment Forecasting

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A Model for Short-Term Institutional Enrollment Forecasting

The Journal of Higher Education ◽

10.1080/00221546.1980.11780061 ◽

1980 ◽

Vol 51 (3) ◽

pp. 314-327 ◽

Cited By ~ 2

Author(s):

William C. Weiler

Keyword(s):

Short Term ◽

Enrollment Forecasting

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

International Research in Higher Education ◽

10.5430/irhe.v4n3p58 ◽

2019 ◽

Vol 4 (3) ◽

pp. 58

Author(s):

Lu Qin ◽

Kyle Shanks ◽

Glenn Allen Phillips ◽

Daphne Bernard

Keyword(s):

Time Series ◽

Historical Data ◽

Moving Average ◽

Arima Model ◽

Forecasting Accuracy ◽

Autoregressive Integrated Moving Average ◽

Enrollment Forecasting ◽

Moving Average Model ◽

The Impact ◽

Practical Impact

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.

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