Analyzing Bangladesh`s Present Patterns in Population Growth and Prediction By ARIMA and Exponential Smoothing Model

Sofi Mahmud Parvez¹ , Nur Hosain Md. Ariful Azim²

Section:Research Paper, Product Type: Journal-Paper
Vol.10 , Issue.3 , pp.41-48, Jun-2023

Online published on Jun 30, 2023

Copyright © Sofi Mahmud Parvez, Nur Hosain Md. Ariful Azim . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Bangladesh has the highest population density in the entire world. It ranks as the eighth largest population nation in the world in South Asia with a population of about 170 million. Bangladesh`s rapid population growth is one of the most significant problems. Currently one of the most populous nations and the country`s major concern is its enormous population. An exponential smoothing and ARIMA (1,1,1) model is used to forecast Bangladesh`s population from 2022 to 2070 using actual data from 1950 to 2021. The population in 2070 is predicted to be between 277 million and 306 million according to our analysis, which used the ARIMA (1,1,1) models and exponential smoothing. We used ARIMA and exponential smoothing models to analyse our time series data, which were chosen based on Bayesian information criterion (BIC) with P-values less than 5% and graphical representation of ACF and PACF plots. These models are used to draw numerical pictures of future population growth and forecast.

Key-Words / Index Term :
ARIMA, Exponential Smoothing model, Population Growth, ACF and PACF

References :
[1] H. Mondol, U. K. Mallick, and M. H. A. Biswas, "Mathematical modeling and predicting the current trends of human population growth in Bangladesh," Modelling, Measurement & Control. D: Manufacturing, Management, Human & Socio-Economic Problems, vol. 39, no. 1, 2018.
[2] M. R. Islam, "Modeling and predicting cumulative population of Bangladesh," American Journal of Computational and Applied Mathematics, vol. 1, no. 2, pp. 98-100, 2011.
[3] World Bank, "The World Bank Population Report.," Available: http://data.worldbank.org/indicator/SP.POP.TOTL.
[4] M. H. A. Biswas, M. Ara, M. N. Haque, and M. A. Rahman, "Application of control theory in the efficient and sustainable forest management," International Journal of Scientific and Engineering Research, vol. 3, p. 2, 2011.
[5] A. N. Wali, E. Kagoyire, and P. Icyingeneye, "Mathematical modeling of Uganda population growth," 2012.
[6] M. H. A. Biswas, L. T. Paiva, and M. D. R. de Pinho, "A SEIR model for control of infectious diseases with constraints," Mathematical Biosciences and Engineering, vol. 11, no. 4, pp. 761-784, 2014.
[7] M. Biswas, "Model and control strategy of the deadly Nipah virus (NiV) infections in Bangladesh," Research & Reviews in Biosciences, vol. 6, no. 12, pp. 370-377, 2012.
[8] T. Islam, D. G. Fiebig, and N. Meade, "Modelling multinational telecommunications demand with limited data," International Journal of Forecasting, vol. 18, no. 4, pp. 605-624, 2002.
[9] J. E. Cohen, "Population growth and earth`s human carrying capacity," Science, vol. 269, no. 5222, pp. 341-346, 1995.
[10] A. Rahman and M. M. Hasan, "Modeling and forecasting of carbon dioxide emissions in Bangladesh using Autoregressive Integrated Moving Average (ARIMA) models," Open Journal of Statistics, vol. 7, no. 4, pp. 560-566, 2017.
[11] G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control. John Wiley & Sons, 2015.
[12] G. G. Judge, W. E. Griffiths, R. C. Hill, H. Lütkepohl, and T.-C. Lee, The theory and practice of econometrics. John wiley & sons, 1991.
[13] C. C. Holt, "Forecasting seasonals and trends by exponentially weighted moving averages," International journal of forecasting, vol. 20, no. 1, pp. 5-10, 2004.
[14] P. R. Winters, "Forecasting sales by exponentially weighted moving averages," Management science, vol. 6, no. 3, pp. 324-342, 1960.