Forecasting the Number of Applicants for Graduate Level Using Time Series Model
Keywords:
Forecasting, Time Series, ARIMA, Exponential Smoothing, Graduate Program ApplicantsAbstract
This research aimed to compare the performance of four time-series forecasting methods, namely the Decomposition Method, Time-Series Regression, Box–Jenkins Method (ARIMA), and Exponential Smoothing, for forecasting the number of graduate program applicants. The data consisted of doctoral and master's degree applicants at King Mongkut’s University of Technology North Bangkok during the academic years 2014-2025. Data were analyzed using Minitab and Microsoft Excel. Forecasting accuracy was evaluated using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The results revealed that the ARIMA model provided the highest forecasting accuracy for both doctoral and master's degree applicants. In conclusion the ARIMA model was found to be the most appropriate and effective method for forecasting the number of graduate program applicants. The forecasting results can support enrollment planning, educational resource management, and policy formulation for graduate education management.
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