FORECASTING OF POPULATION AND ECONOMIC CHARACTERISTICS WITH ARIMA MODELS IN INDIA

The Auto-regressive Integrated Moving Averages (ARIMA) and Holt-Winters Exponential Smoothing Models are discussed in this article. We also used the AIC and BIC to find the best-fitting ARIMA model for the data and provide population and economic forecasts for future years. For forecasting, we also apply the Holt-Winters Exponential Smoothing Model. The ARIMA (0, 2, 5), (0, 2, 5), (0, 2, 4), (1, 2, 2) and (0, 2, 2) models were also found to be the best-fitting models for India's Total, Urban and Rural population, GDP and Age Dependency Ratio. The ARIMA model is the best-fitted model compared to the Holt-Winters model for the forecasting. The ARIMA model underestimates the total population, whereas the Holt-Winters model overestimates it. Both models overestimate for urban populations and GDP. Rural population and the Age Dependency Ratio are underestimated by both models.