Forecasting Total Fertility Rate in Mali Using a Machine Learning Technique

In this research article, the ANN approach was applied to analyze TFR in Mali. The employed data covers the period 1960-2018 and the out-of-sample period ranges over the period 2019-2030. The residuals and forecast evaluation criteria (Error, MSE and MAE) of the applied model indicate that the model is stable in forecasting TFR in Mali. The results of the study indicate that annual total fertility rates in Mali are likely to rise slightly over the out-of-sample period. Therefore, the Mali government is encouraged to concentrate its effort on addressing barriers to accessing sexual and reproductive health (SRH) services among adolescents and young adults, and prioritize women empowerment.