• Ambati Sushmitha
• Ravipalli Shreya
• Donakanti Sharmila
• Dr. M.V. Krishna Rao

Life expectancy, a critical measure of public health and social progress, indicates the average lifespan of a population and has become essential for shaping healthcare policies, economic planning, and demographic research. Predicting life expectancy trends allows stakeholders to prepare for future population dynamics, allocate resources, and implement necessary interventions effectively. Early Life Expectancy models primarily relied on mortality rates within specific populations; however, advancements in forecasting now emphasize the importance of additional factors—such as education, economic conditions, and healthcare access—in accurately projecting life expectancy trends. In this study, life expectancy values of several countries are forecasted utilizing: Principal Component Analysis (PCA), Autoregressive Moving Average (ARMA), and Autoregressive Integrated Moving Average (ARIMA) with different differencing order. Then it compares the performance of these methods to understand predicting future life expectancy trends.

Life expectancy prediction, Principal Component Analysis (PCA), Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), time series analysis, demographic forecasting.

"Global Life Expectancy Prediction Using Nonstationary Time Series Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.i369-i377, March-2025, Available :http://www.jetir.org/papers/JETIR2503853.pdf

"Global Life Expectancy Prediction Using Nonstationary Time Series Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppi369-i377, March-2025, Available at : http://www.jetir.org/papers/JETIR2503853.pdf