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<p>The present study was conducted to compare two different models, Arima and Holt's model. For this study, serial data on TFR was collected from 1995–96 to 2019–20 (26&#xa0;years). Two separate models were used to find the best model for future forecasting of total yield rates in India: autoregressive integrated moving average (ARIMA) and Holt's method. The result shows that the ARIMA model is better than the Holt model in predicting the future crop yield rate in India, with smaller values ​​of the mean square error (MSE), mean square error (RMSE), and absolute error. (MAE), mean percentage absolute error (MAPE). The performance of the ARIMA model is more accurate than the Holt model, with the lowest MSE (0.000179) RMSE (0.013382), MAE (0.006440), and MAPE (0.252520). Therefore, this study will be useful for policymakers to have more informed ways for better and more efficient planning and allocation of resources in a region that can help to create more schools, colleges, hospitals, and parks, or to expand access to affordable childcare.</p>

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A comparative analysis of the Holt and ARIMA models for predicting the future total fertility rate in India

  • 期刊论文

  • 发表日期：

    2025年1月3日

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  • Anuj Kumar,
  • Bhagchand Meena

摘要

The present study was conducted to compare two different models, Arima and Holt's model. For this study, serial data on TFR was collected from 1995–96 to 2019–20 (26 years). Two separate models were used to find the best model for future forecasting of total yield rates in India: autoregressive integrated moving average (ARIMA) and Holt's method. The result shows that the ARIMA model is better than the Holt model in predicting the future crop yield rate in India, with smaller values ​​of the mean square error (MSE), mean square error (RMSE), and absolute error. (MAE), mean percentage absolute error (MAPE). The performance of the ARIMA model is more accurate than the Holt model, with the lowest MSE (0.000179) RMSE (0.013382), MAE (0.006440), and MAPE (0.252520). Therefore, this study will be useful for policymakers to have more informed ways for better and more efficient planning and allocation of resources in a region that can help to create more schools, colleges, hospitals, and parks, or to expand access to affordable childcare.