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SutteARIMA: A Novel Method for Forecasting the Infant Mortality Rate in Indonesia

Ansari Saleh Ahmar1,2,*, Eva Boj del Val3, M. A. El Safty4, Samirah AlZahrani4, Hamed El-Khawaga5,6
1 Business School, Faculty of Economics and Business, Universitat de Barcelona, Barcelona, 08034, Spain
2 Department of Statistics, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Makassar, 90224, Indonesia
3 Department of Economic, Financial and Actuarial Mathematics, Faculty of Economics and Business, Universitat de Barcelona, Barcelona, 08034, Spain
4 Department of Mathematics and Statistics, Colleage of Sciences, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
5 Department of Statistics, Mathematics, and Insurance, Faculty of Commerce, Tanta University, Egypt
6 Department of Economics and Finance, College of Business Administration, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
* Corresponding Author: Ansari Saleh Ahmar. Email:
(This article belongs to this Special Issue: Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)

Computers, Materials & Continua 2022, 70(3), 6007-6022. https://doi.org/10.32604/cmc.2022.021382

Received 12 June 2021; Accepted 17 August 2021; Issue published 11 October 2021

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.

Keywords

Forecasting; infant mortality rate; ARIMA; NNAR; holt-winters; SutteARIMA

Cite This Article

A. Saleh Ahmar, E. Boj del Val, M. A. El Safty, S. AlZahrani and H. El-Khawaga, "Suttearima: a novel method for forecasting the infant mortality rate in indonesia," Computers, Materials & Continua, vol. 70, no.3, pp. 6007–6022, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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