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

doi:10.60084/ijds.v4i1.425

Authors

Ghifari Maulana Idroes Department of Nuclear Engineering and Engineering Physics, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
Fatih Avicenna Hizir School of Data Science, Mathematics, and Informatics, IPB University, Bogor 16680, Indonesia
Muhammad Zhafran Abiyyu Department of International Relations, Faculty of Social and Political Sciences, Universitas Jenderal Soedirman, Purwokerto 53122, Indonesia

DOI:

https://doi.org/10.60084/ijds.v4i1.425

Keywords:

ARIMA , ASEAN, Log transformation, Univariate forecasting, Forecast accuracy

Abstract

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.

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References

Lee, R. (2025). Forecasting Population in an Uncertain World: Approaches, New Uses, and Troubling Limitations, Population and Development Review, Vol. 51, No. 1, 491–518. doi:10.1111/padr.12674.
Bhattacharjee, N. V, Schumacher, A. E., Aali, A., Abate, Y. H., Abbasgholizadeh, R., Abbasian, M., Abbasi-Kangevari, M., Abbastabar, H., Abd ElHafeez, S., Abd-Elsalam, S., Abdollahi, M., Abdollahifar, M.-A., Abdoun, M., Abdullahi, A., Abebe, M., Abebe, S. S., Abiodun, O., Abolhassani, H., Abolmaali, M., et al. (2024). Global Fertility in 204 Countries and Territories, 1950–2021, with Forecasts to 2100: A Comprehensive Demographic Analysis for the Global Burden of Disease Study 2021, The Lancet, Vol. 403, No. 10440, 2057–2099. doi:10.1016/S0140-6736(24)00550-6.
Jean Yeung, W.-J., and Abalos, J. B. (2025). Social Determinants of Low Fertility in Asia: A Comparative Review of Trends in East, Southeast and South Asia, The Lancet Regional Health - Western Pacific, Vol. 65, 101724. doi:10.1016/j.lanwpc.2025.101724.
Sakamoto, R. (2025). Aging in Southeast Asia and Japan: Challenges and Opportunities, Geriatrics & Gerontology International, Vol. 25, No. 7, 837–854. doi:10.1111/ggi.70062.
Wiśniowski, A., and Raymer, J. (2025). Multiregional Population Forecasting: A Unifying Probabilistic Approach for Modelling the Components of Change, European Journal of Population, Vol. 41, No. 1, 11. doi:10.1007/s10680-025-09729-7.
Mohan, A., Jaihind Jothikaran, T. A., Gudi, N., A K, A., and Ashok, L. (2025). Growing Old before Growing Rich: A Scoping Review on Ageing-Related Policy Interventions from the WHO Southeast Asian Region, BMJ Open, Vol. 15, No. 1, e079621. doi:10.1136/bmjopen-2023-079621.
United Nations Population Division, D. of E. and S. A. (2022). World Population Prospects 2022: Methodology of the United Nations Population Estimates and ProjectionsNew York, United Nations.
Raftery, A. E., and Ševčíková, H. (2023). Probabilistic Population Forecasting: Short to Very Long-Term, International Journal of Forecasting, Vol. 39, No. 1, 73–97. doi:10.1016/j.ijforecast.2021.09.001.
Kontopoulou, V. I., Panagopoulos, A. D., Kakkos, I., and Matsopoulos, G. K. (2023). A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks, Future Internet, Vol. 15, No. 8, 255. doi:10.3390/fi15080255.
Taslim, D. G., and Murwantara, I. M. (2024). Comparative Analysis of ARIMA and LSTM for Predicting Fluctuating Time Series Data, Bulletin of Electrical Engineering and Informatics, Vol. 13, No. 3, 1943–1951. doi:10.11591/eei.v13i3.6034.
Ab Rashid, M. A., Ahmad Zaki, R., Wan Mahiyuddin, W. R., and Yahya, A. (2023). Forecasting New Tuberculosis Cases in Malaysia: A Time-Series Study Using the Autoregressive Integrated Moving Average (ARIMA) Model, Cureus, Vol. 15, No. 9, e44676. doi:10.7759/cureus.44676.
Schaffer, A. L., Dobbins, T. A., and Pearson, S.-A. (2021). Interrupted Time Series Analysis Using Autoregressive Integrated Moving Average (ARIMA) Models: A Guide for Evaluating Large-Scale Health Interventions, BMC Medical Research Methodology, Vol. 21, No. 1, 58. doi:10.1186/s12874-021-01235-8.
Wilson, T., Grossman, I., Alexander, M., Rees, P., and Temple, J. (2022). Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs, Population Research and Policy Review, Vol. 41, No. 3, 865–898. doi:10.1007/s11113-021-09671-6.
Lütkepohl, H., and Xu, F. (2012). The Role of the Log Transformation in Forecasting Economic Variables, Empirical Economics, Vol. 42, No. 3, 619–638. doi:10.1007/s00181-010-0440-1.
Ma, J., Chen, Q., Chen, X., Fan, J., and Li, X. (2025). An Inevitably Ageing World: Analysis on the Evolutionary Pattern of Age Structure in 200 Countries, Royal Society Open Science, Vol. 12, No. 4. doi:10.1098/rsos.241988.
Ritchie, H., Rosado, P., and Roser, M. (2023). Population Growth, Our World in Data.
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., et al. (2020). Array Programming with NumPy, Nature, Vol. 585, No. 7825, 357–362. doi:10.1038/s41586-020-2649-2.
Makridakis, S., Spiliotis, E., and Assimakopoulos, V. (2018). The M4 Competition: Results, Findings, Conclusion and Way Forward, International Journal of Forecasting, Vol. 34, No. 4, 802–808. doi:10.1016/j.ijforecast.2018.06.001.
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.), Wiley, Hoboken, NJ.
Hyndman, R. J., and Khandakar, Y. (2008). Automatic Time Series Forecasting: The Forecast Package for R, Journal of Statistical Software, Vol. 27, No. 3, 1–22. doi:10.18637/jss.v027.i03.
Granger, C. W. J., and Newbold, P. (1986). Forecasting Economic Time Series (2nd ed.), Academic Press, Orlando, FL.
Brockwell, P. J., and Davis, R. A. (2016). Introduction to Time Series and Forecasting (3rd ed.), Springer International Publishing, Cham. doi:10.1007/978-3-319-29854-2.
Seabold, S., and Perktold, J. (2010). Statsmodels: Econometric and Statistical Modeling with Python, Proceedings of the 9th Python in Science Conference, 92–96. doi:10.25080/Majora-92bf1922-011.
Hyndman, R. J., and Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.), OTexts, Melbourne.
Dickey, D. A., and Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root, Journal of the American Statistical Association, Vol. 74, No. 366a, 427–431. doi:10.1080/01621459.1979.10482531.
Akaike, H. (1974). A New Look at the Statistical Model Identification, IEEE Transactions on Automatic Control, Vol. 19, No. 6, 716–723. doi:10.1109/TAC.1974.1100705.
Melina, M., Sukono, S., Napitupulu, H., Mohamed, N., Chrisnanto, Y. H., Hadiana, A. I., Kusumaningtyas, V. A., and Nabilla, U. (2024). Comparative Analysis of Time Series Forecasting Models Using Arima and Neural Network Autoregression Methods, BAREKENG: Jurnal Ilmu Matematika Dan Terapan, Vol. 18, No. 4, 2563–2576. doi:10.30598/barekengvol18iss4pp2563-2576.
LJUNG, G. M., and BOX, G. E. P. (1978). On a Measure of Lack of Fit in Time Series Models, Biometrika, Vol. 65, No. 2, 297–303. doi:10.1093/biomet/65.2.297.
Jarque, C. M., and Bera, A. K. (1980). Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals, Economics Letters, Vol. 6, No. 3, 255–259. doi:10.1016/0165-1765(80)90024-5.
Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica, Vol. 50, No. 4, 987. doi:10.2307/1912773.
Hyndman, R. J., and Koehler, A. B. (2006). Another Look at Measures of Forecast Accuracy, International Journal of Forecasting, Vol. 22, No. 4, 679–688. doi:10.1016/j.ijforecast.2006.03.001.
Makridakis, S., Wheelwright, S. C., and Hyndman, R. J. (1998). Forecasting: Methods and Applications (3rd ed.), Wiley, New York.
Reher, D. S. (2004). The Demographic Transition Revisited as a Global Process, Population, Space and Place, Vol. 10, No. 1, 19–41. doi:10.1002/psp.313.
Gerland, P., Raftery, A. E., Ševčíková, H., Li, N., Gu, D., Spoorenberg, T., Alkema, L., Fosdick, B. K., Chunn, J., Lalic, N., Bay, G., Buettner, T., Heilig, G. K., and Wilmoth, J. (2014). World Population Stabilization Unlikely This Century, Science, Vol. 346, No. 6206, 234–237. doi:10.1126/science.1257469.
Swanson, D. A., and Tayman, J. (2025). On Using ARIMA Model Confidence Intervals Applied to Population Projections Based on the Components of Change: A Case Study for the World Population, Statistics in Transition New Series, Vol. 26, No. 4, 1–14. doi:10.59139/stattrans-2025-036.