Forecasting Total Fertility Rate (TFR) in Chad Using a Machine Learning Approach

In this research article, the ANN approach was applied to analyze TFR in Chad. 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 Chad. The results of the study indicate that annual total fertility rates in Chad are likely to increase over the out-of-sample period. Therefore, authorities in Chad are encouraged to (1) increase demand creation activities for family planning services and improve accessibility of sexual and reproductive health (SRH) services among adolescents and young adults, and (2) continuous empowerment of women through education, labor participation and promoting women’s rights.