Methodological Evaluation of Time-Series Forecasting Models for Risk Reduction in Process-Control Systems in Kenya: An Engineering Perspective

This study examines process-control systems in Kenya to evaluate risk reduction through time-series forecasting models. A comparative analysis of various time-series forecasting models including ARIMA (AutoRegressive Integrated Moving Average) was conducted. The study employed robust standard errors to quantify the uncertainty associated with model predictions. The ARIMA model showed a reduction in forecast error variance by approximately 15% compared to simpler models, indicating improved risk assessment and control mechanisms. Time-series forecasting models have been validated for their effectiveness in reducing risks within process-control systems. The ARIMA model is recommended for further implementation due to its superior performance metrics. Further research should explore the integration of machine learning techniques with time-series models to enhance predictive accuracy and adaptability. Process-Control Systems, Time-Series Forecasting, Risk Reduction, Engineering Applications, ARIMA Model The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.