Time-Series Forecasting Model Evaluation for Yield Improvement in Ghanaian District Hospitals Systems,

This study focuses on evaluating the performance of time-series forecasting models in predicting yield improvement for district hospitals in Ghana. A comparative analysis was conducted using autoregressive integrated moving average (ARIMA) models. The dataset comprised monthly hospital performance data from to . The ARIMA model demonstrated the highest predictive accuracy with a mean absolute error of 5% and confidence intervals indicating robustness in forecast reliability. ARIMA models were found to be effective for forecasting yield improvement, providing actionable insights for district hospitals. District hospital managers should adopt or refine ARIMA-based forecasting systems for more precise resource planning and service delivery enhancements. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.