Forecasting Enrollment Trends in a Higher Education Institution Using Time Series and Autoregressive Integrated Moving Average Models
DOI:
https://doi.org/10.65023/jherd.v10i2.254Abstract
This research examines student enrollment patterns at one of the institutions of higher learning in Misamis Occidental from 2022 to 2025 and predicts future enrollment rates from 2026 to 2030 through Time Series Analysis and the Autoregressive Integrated Moving Average (ARIMA) model. Information collected from the institution's registrar demonstrates oscillating trends in enrollment, evident decreases in 2023 and 2024 followed by a strong recovery in 2025. ARIMA was utilized to forecast enrollment for the subsequent five years, exhibiting cyclical behavior based on both institutional and external socio-economic factors. The findings underscore the potential of ARIMA in aiding data-driven decision-making in institutional planning, resource management, and policy development. This research adds to the paucity of local literature on statistical forecasting in Philippine state colleges and offers an applied instrument for education leaders to react affirmatively to enrollment trends. Subsequent studies are invited to develop hybrid forecasting models and include more general variables like economic indicators, demographic changes, and student tastes to improve projection accuracy.
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