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Vol. 12, Special Issue 7 (2023)

Comparative analysis of the time series ARIMA model over the AR, MA, and Holt's models for future prediction of total fertility rate in India

Author(s):
Anuj Kumar and Bhupendra Meena
Abstract:
This research compares the AR, MA, Holt's, and ARIMA models. Time series data on TFR were collected for the 72-year research period between 1949-1950 and 2021-2022. To determine the most accurate model for future prediction of the TFR in India, four distinct models were used: AR, MA, Holt's, and ARIMA. The future prediction Measure errors, namely, mean absolute percentage error (MAPE), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), were used as model selection criteria. The study shows that the ARIMA performs better and is more accurate than three other models, namely the AR, MA, and Holt's models, with the lowest values being MAE=0.003774, MAPE=0.010079, RMSE=0.009707, and MSE=0.000094. The study findings will be used to future prediction fertility patterns in the country. The economic, social, cultural, and political spheres are among those where population changes have an impact on human activity. The government will be able to allocate resources and prepare for children's services in the future thanks to TFR's future forecast.
Pages: 912-920  |  183 Views  129 Downloads
How to cite this article:
Anuj Kumar and Bhupendra Meena. Comparative analysis of the time series ARIMA model over the AR, MA, and Holt's models for future prediction of total fertility rate in India. The Pharma Innovation Journal. 2023; 12(7S): 912-920.
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