Abstract
This study focuses on the novel forecasting method (SutteARIMA) and its application in predicting Infant Mortality Rate data in Indonesia. It undertakes a comparison of the most popular and widely used four forecasting methods: ARIMA, Neural Networks Time Series (NNAR), Holt-Winters, and SutteARIMA. The data used were obtained from the website of the World Bank. The data consisted of the annual infant mortality rate (per 1000 live births) from 1991 to 2019. To determine a suitable and best method for predicting Infant Mortality rate, the forecasting results of these four methods were compared based on the mean absolute percentage error (MAPE) and mean squared error (MSE). The results of the study showed that the accuracy level of SutteARIMA method (MAPE: 0.83% and MSE: 0.046) in predicting Infant Mortality rate in Indonesia was smaller than the other three forecasting methods, specifically the ARIMA (0.2.2) with a MAPE of 1.21% and a MSE of 0.146; the NNAR with a MAPE of 7.95% and a MSE of 3.90; and the Holt-Winters with a MAPE of 1.03% and a MSE: of 0.083.
Highlights
In this era of globalization and continuous industrial development, every human being wants to get information as fast as possible
The purpose of this research is to model the infant mortality rate data and find the best model to predict this problem in the future
To determine which prediction model is more suitable and precise in predicting data, the mean absolute percentage error (MAPE) and mean squared error (MSE) values of each of the forecasting methods used are calculated and the results are compared according to the predetermined performance criteria
Summary
In this era of globalization and continuous industrial development, every human being wants to get information as fast as possible. Statistics, which is one of the fields of science related to the acquisition of information in several scientific disciplines, has made progress This advancement usually requires different methods of solving different problems. The statistical method that is often used to obtain future information is known as forecasting or predictive analysis. Given the importance of this infant mortality rate and to achieve one of the targets of the sustainable developmental goals (SDGs) in the health sector of the Republic of Indonesia, namely by 2030, to end preventable deaths of newborns and children under five, with all countries trying to reduce the Neonatal Mortality Rate at least up to 12 per 1000 KH (Live Birth) and the under-five mortality rate of 25 per 1000, a suitable statistical method is needed in order to provide information in the future to minimize infant mortality. SutteARIMA is used in this study because the SutteARIMA method is a new forecasting method that has a good level of accuracy in some forecasting data [12]
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