Time Series Analysis and Forecasting of Maternal Mortality in Somalia

Maternal mortality remains a critical indicator of health system performance and socioeconomic development, particularly in fragile and conflict-affected settings such as Somalia. This study presents a comparative evaluation of single and hybrid time series models for forecasting the maternal mortality ratio (MMR) in Somalia. A comprehensive set of time series configurations was examined, including six single models—AutoRegressive Integrated Moving Average (ARIMA), Error–Trend–Seasonality (ETS), Trigonometric seasonality Box–Cox transformation (TBATS), Theta, AutoRegressive Fractionally Integrated Moving Average (ARFIMA), and Neural Network AutoRegression (NNAR)—as well as ten hybrid model combinations. The dataset spans from 1985 to 2023, comprising 39 annual observations, and was divided into a training set (1985–2015) and a testing set (2016–2023) to enable out-of-sample validation. Forecasting performance was assessed using Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (sMAPE), and Theil’s U statistic. Stationarity testing indicated that the MMR series was non-stationary, necessitating first differencing prior to model estimation. Results demonstrate that the ARIMA(0,1,1) with drift model achieved the highest forecasting accuracy among single models (MAPE = 4.38%), outperforming both alternative single models and all hybrid configurations during the validation period. Although selected hybrid models, particularly ARIMA–ETS, exhibited competitive performance, none surpassed the predictive accuracy of the ARIMA model. Forecasts for the period 2024–2030 indicate a gradual decline in maternal mortality under baseline assumptions, accompanied by wide prediction intervals reflecting historical volatility and structural uncertainty. These findings provide a quantitative framework to support evidence-based maternal health planning and strategic resource allocation in Somalia. The study contributes to the advancement of epidemiological forecasting in fragile contexts and offers actionable insights for monitoring progress toward Sustainable Development Goal (SDG) 3.1