Application of Forecasting Model to Study the Population Growth of India | Population Forecasting
2Application of Forecasting Model to Study the Population Growth of India
Layman Abstract : This study compares three forecasting methods—ARIMA, ETS, and Kalman Filtering—to predict India’s population from (Population Growth )2021 to 2091. ARIMA suggests a population decline (Population Forecasting) after 2051, while ETS and Kalman Filtering predict continuous growth. ARIMA provides the most accurate short-term forecasts, while ETS is better for long-term trends. Kalman Filtering has the highest uncertainty. The study also finds that a machine learning model, XGBoost, is the most reliable for long-term predictions. The findings highlight the importance of choosing the right forecasting model based on time frame and accuracy needs.
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View Book: https://doi.org/10.9734/bpi/mcsru/v4/4778
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