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Application of GMDH-type ANN model for Prediction of Total Fertility Rate: A case study of India

Minakshi Mishra, Anuj Kumar

Abstract



The present study aimed to compare the group method of data handling type artificial neural network (GMDH-type ANN) model and autoregressive integrated moving average (ARIMA) model for prediction of India’s total fertility rate (TFR). In this study, time series TFR data was collected from the period 1995–96 to 2019–2020. The ARIMA (1,2,2) model was found to have lower Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) values, making them more acceptable. After that, GMDH-type ANN model was fitted and to check the adequacy of these two models using different error measures such as the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and scatter index (SI). The GMDH-type ANN model is superior over ARIMA (1,2,2) model. Thus, the GMDH-type ANN model was used to predict the TFR of India for the next 10 years and the results indicated that the TFR will decline in upcoming years. The government will use the information regarding the prediction of TFR to allocate the forthcoming resources and also make decisions for people`s welfare.

Keywords


Total Fertility Rate, ARIMA, GMDH-type ANN,

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