Time-Series Forecasting Model Evaluation for Adoption Rates in Manufacturing Systems of Rwanda

The adoption rates of manufacturing systems in Rwanda have been observed to vary significantly across different regions and industries. A mixed-method approach was employed, including quantitative data analysis using the ARIMA (AutoRegressive Integrated Moving Average) model to forecast future adoption trends based on historical data from selected industries. The ARIMA model demonstrated an R² value of 0.85 and a confidence interval for its predictions at ±10%, indicating moderate accuracy in forecasting adoption rates over the next five years. The time-series analysis revealed that technological investment and regulatory support were key factors driving higher adoption rates, though variability across sectors remains significant. Further research should explore additional influencing variables such as market size and infrastructure availability to enhance model accuracy. Rwanda, Manufacturing Systems, Adoption Rates, Time-Series Forecasting, ARIMA Model The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.