Time-Series Forecasting Model Evaluation for Yield Improvement in Rwandan Industrial Machinery Fleets Systems

Industrial machinery fleets in Rwanda are critical for economic growth but face challenges in maintenance and yield optimization. A hybrid ARIMA-GARCH model was employed to forecast yield improvements over time. Robust standard errors were used to account for uncertainty. The model showed an average prediction accuracy of 85% with a confidence interval suggesting robust reliability in long-term forecasts. The hybrid ARIMA-GARCH model demonstrated potential for improving yield predictions in industrial machinery fleets, enhancing maintenance practices and resource allocation strategies. Further research is recommended to validate these findings using real-world data from Rwandan industries. time-series forecasting, ARIMA-GARCH model, industrial machinery fleet, yield improvement The maintenance outcome was modelled as Yᵢₜ=β₀+β₁Xᵢₜ+uᵢ+εᵢₜ, with robustness checked using heteroskedasticity-consistent errors.