African Civil Law Studies | 09 September 2001
Methodological Evaluation of Time-Series Forecasting Models for Risk Reduction in Process-Control Systems in Kenya: An Engineering Perspective
O, s, c, a, r, M, b, o, l, e, w, a, ,, W, i, l, f, r, e, d, O, w, i, l, i, w, a
Abstract
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 $Y<em>{it}=\beta</em>0+\beta<em>1X</em>{it}+u<em>i+\varepsilon</em>{it}$, with robustness checked using heteroskedasticity-consistent errors.