Short-Term Reliability and Long-Term Limits of Stable Log-ARIMA for Population Forecasting Across Southeast Asia - Ghifari Maulana Idroes; Fatih Avicenna Hizir; Muhammad Zhafran Abiyyu (Infolitika Journal of Data Science, 2026)
Population forecasting is essential for long-term planning in labor markets, healthcare, education, infrastructure, and social protection. This study evaluates the short-term reliability and long-term limits of Stable Log-ARIMA for population forecasting across eleven Southeast Asian countries. Annual population data from 1950 to 2023 were used for model fitting and validation, while United Nations World Population Prospects (UN WPP) projections from 2024 to 2100 were used as the long-term demographic benchmark. The model was fitted to logarithmic population series using constrained ARIMA estimation, with model order selected by the Akaike Information Criterion. Short-term validation, using 1950–2010 for training and 2011–2023 for validation, yielded an average MAPE of 2.01%, indicating strong short-term forecasting performance. However, comparison with UN WPP projections showed increasing long-term divergence, with the regional forecast increasingly overestimating population toward 2100. These findings indicate that Stable Log-ARIMA is useful as a transparent and parsimonious short-term statistical baseline. Still, it should not be interpreted as a substitute for structurally informed demographic projection models that incorporate fertility, mortality, migration, and age-structure dynamics.