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

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 andwidely 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 InfantMortality 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%andMSE:0.046)in predicting InfantMortality 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 aMAPE of 1.03%and aMSE:of 0.083.

Prediction of BRIC Stock Price Using ARIMA,SutteARIMA,and Holt-Winters

The novel coronavirus has played a disastrous role in many countries worldwide.The outbreak became a major epidemic,engulfing the entire world in lockdown and it is now speculated that its economic impact might be worse than economic deceleration and decline.This paper identifies two different models to capture the trend of closing stock prices in Brazil(BVSP),Russia(IMOEX.ME),India(BSESN),and China(SSE),i.e.,(BRIC)countries.We predict the stock prices for three daily time periods,so appropriate preparations can be undertaken to solve these issues.First,we compared the ARIMA,SutteARIMA and Holt-Winters(H-W)methods to determine the most effective model for predicting data.The stock closing price of BRIC country data was obtained from Yahoo Finance.That data dates from 01 November 2019 to 11 December 2020,then divided into two categories-training data and test data.Training data covers 01 November 2019 to 02 December 2020.Seven days(03December 2020 to 11December 2020)of datawas tested to determine the accuracy of the models using training data as a reference.To measure the accuracy of the models,we obtained the means absolute percentage error(MAPE)and mean square error(MSE).Prediction model Holt-Winters was found to be the most suitable for forecasting the Brazil stock price(BVSP)while MAPE(0.50)and MSE(579272.65)with Holt-Winters(smaller than ARIMA and SutteARIMA),model SutteARIMA was found most appropriate to predict the stock prices of Russia(IMOEX.ME),India(BSESN),and China(SSE)when compared to ARIMA and Holt-Winters.MAPE andMSE with SutteARIMA:Russia(MAPE:0.7;MSE:940.20),India(MAPE:0.90;MSE:207271.16),and China(MAPE:0.72;MSE:786.28).Finally,Holt-Winters predicted the daily forecast values for the Brazil stock price(BVSP)(12 December to 14 December 2020 i.e.,115757.6,116150.9 and 116544.1),while SutteARIMA predicted the daily forecast values of Russia stock prices(IMOEX.ME)(12 December to 14 December 2020 i.e.,3238.06,3241.54 and 3245.01),India stock price(BSESN)(12 December to 14 December 2020 i.e.,.45709.38,45828.71 and 45948.05),and China stock price(SSE)(11 December to 13 December 2020 i.e.,3397.56,3390.59 and 3383.61)for the three time periods.