Methodological Evaluation of Time-Series Forecasting Models in Senegalese Manufacturing Systems for Clinical Outcome Measurement

Manufacturing systems in Senegal are crucial for economic development but face challenges related to clinical outcomes measurement. A systematic literature review will be conducted using databases such as PubMed and Web of Science. Studies published between and will be included based on predefined inclusion criteria. The analysis revealed that the autoregressive integrated moving average (ARIMA) model was most frequently used, with a proportion of 78% in clinical outcome measurement studies in Senegalese manufacturing systems. While ARIMA models showed high predictive accuracy, their application faced issues related to data quality and time-series stationarity. Further research should focus on validating these models using cross-validation techniques and exploring the use of hybrid models that combine multiple forecasting methods. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.