Forecasting Total Fertility Rate in Mali Using a Machine Learning Technique
Abstract
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.
Country : Zimbabwe
1 Dr. Smartson. P. NYONI2 Tatenda. A. CHIHOHO3 Thabani NYONI
ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
Independent Health Economist, Zimbabwe
SAGIT Innovation Center, Harare, Zimbabwe
IRJIET, Volume 5, Issue 8, August 2021 pp. 252-255
