Forecast of demographic variables using the ARIMA Model in India

Bheemanna ., MN Megeri, Huchesh H Budihal
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Abstract

Life expectancy at birth reflects the overall mortality level of a population. Three important demographic indicators—Life Expectancy at Birth, Death Rate, and Infant Mortality Rate (IMR)—were examined for 1971 to 2020 and projected in this study for the years 2021 to 2030. The projections that went along with the forecasts were created using statistical models. The Auto-regressive Integrated Moving Averages (ARIMA) is discussed in this article for the selected demographic variables. We also used the AIC and BIC to find the best-fitting ARIMA model for the data and provide the life expectancy at birth, Death rate and IMR forecasts for future years. The ARIMA (0, 2, 1), (3, 1, 0), and (3, 1, 0) models were also found to be the best-fitting models for India's Life expectancy at birth, Death rate and IMR respectively. The life expectancy at birth is best fits compared to other variables based on the MAPE values.
用ARIMA模型预测印度人口变量
出生时的预期寿命反映了人口的总体死亡率。研究了1971年至2020年的三个重要人口指标——出生时预期寿命、死亡率和婴儿死亡率,并在本研究中预测了2021年至2030年的情况。伴随预测而来的预测是用统计模型创建的。本文讨论了所选人口统计变量的自回归综合移动平均线(ARIMA)。我们还使用AIC和BIC找到最适合数据的ARIMA模型,并提供未来几年的出生时预期寿命、死亡率和IMR预测。ARIMA(0,2,1)、(3,1,0)和(3,1,0)模型也分别被发现是最适合印度出生时预期寿命、死亡率和综合死亡率的模型。与基于MAPE值的其他变量相比,出生时的预期寿命是最合适的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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