Forecasting Candidate Subscription into The University of Africa, Toru-Orua Using ARIMA Model
Keywords:
ARIMA Model, Candidate Subscription, Forecasting, Stationarity, Time-Series AnalysisAbstract
This study examined candidate subscription into the University of Africa, Toru-Orua (UAT), Bayelsa State, Nigeria, using the autoregressive integrated moving average (ARIMA) modelling approach. The purpose was to describe the annual subscription pattern from 2017 to 2025, assess the time-series properties of the subscription series, identify a statistically adequate ARIMA model, and forecast the subscription for 2026 to 2030. A retrospective time-series design was adopted because the variable of interest was observed sequentially by admission year. The study data consisted of annual candidate subscription values for 2017-2025. The analysis involved trend inspection, computation of annual growth, stationarity testing with the Augmented Dickey-Fuller procedure, comparison of candidate ARIMA models using information criteria, residual diagnostic checking, and five-year forecasting. The series showed a clear upward movement from 518 candidates in 2017 to 2,848 candidates in 2025, with a temporary decline in 2020. The level and first-differenced series were non-stationary, while the second-differenced series was stationary at the 5% level. Among the candidate models considered, ARIMA (0,2,0) was selected because it produced the most favourable information criteria and acceptable residual diagnostics. The forecast indicated that candidate subscription would increase from 3,342 in 2026 to 5,318 in 2030, although the 95% forecast interval widened progressively as the forecast horizon increased. The study concludes that candidate subscription into UAT is expected to maintain an upward path within the forecast period, requiring proactive planning for admission processing, staffing, physical infrastructure and digital admission support systems.
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