Time-Series Forecasting Model Evaluation in Process-Control Systems: A Case Study of Efficiency Gains in Tanzania

This study examines process-control systems in Tanzania to evaluate time-series forecasting models for measuring efficiency gains. A case study approach was employed, utilising a time-series forecasting model (e.g., ARIMA) to analyse data from process-control systems in Tanzania. Robust standard errors were used for inference. The analysis revealed an average efficiency gain of 15% over the forecast period, with significant reductions in variability attributed to model improvements. The findings underscore the effectiveness and reliability of time-series forecasting models in enhancing process-control systems' efficiency in Tanzanian settings. Recommendation is for continued use and refinement of these models within similar contexts, potentially leading to broader application across Tanzania's water resources sector. Process-Control Systems, Time-Series Forecasting, Efficiency Gains, ARIMA Model, Robust Standard Errors The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.