SutteARIMA: A Novel Method for Forecasting the Infant Mortality Rate in Indonesia
Source abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Forecasting Techniques and Applications · Big Data and Business Intelligence · Air Quality Monitoring and Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (20)
Show source-provided references
1. Critical analysis of Big data challenges and analytical methods10.1016/j.jbusres.2016.08.001 ↗
2. The lure of statistics in data mining
3. Edwards · What is Predictive Analytics? Transforming Data into Future Insights · 2019
4. Marketing research: The application of auto sales forecasting software to optimize product marketing strategies10.35877/454RI.asci1124 ↗
5. Predicting futures price and contract portfolios using the ARIMA model: A case of Nigeria's bonny light and forcados10.35877/454RI.qems139 ↗
6. Public health application of predictive modeling: An example from farm vehicle crashes10.1186/s40621-019-0208-9 ↗
7. Modeling uncertainty knowledge of the topological methods10.46753/pjaa.2021.v08i01(i).009 ↗
8. Topological modeling for symptom reduction of coronavirus
9. Proyeksi angka kematian bayi di rumah sakit X kabupaten kediri dengan single exponential smoothing10.20527/jbk.v6i2.8925 ↗
10. Forecasting Indian infant mortality rate: An application of autoregressive integrated moving average model10.4103/jfcm.JFCM_51_18 ↗
11. Time series prediction of under-five mortality rates for Nigeria: Comparative analysis of artificial neural networks, holt-winters exponential smoothing and autoregressive integrated moving average models10.1186/s12874-020-01159-9 ↗
12. The date predicted 200.000 cases of COVID-19 in Spain10.35877/454RI.asci22102 ↗
13. Ahmar · Pendeteksian dan Pengoreksian Data Yang Mengandung Additive Outlier (AO) Pada Model ARIMA(p, d, q) · 2013
14. Wei · T ime Series Analysis: Univariate and Multivariate Methods · 2006
15. Eksplorasi metode triple exponential smoothing pada peramalan jumlah penggunaan air bersih di PDAM kota makassar10.35580/variansiunm14641 ↗
16. Perbandingan metode jaringan syaraf tiruan backpropagation dan learning vector quantization pada pengenalan wajah
17. SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain10.1016/j.scitotenv.2020.138883 ↗
18. Forecast error calculation with mean squared error (MSE) and mean absolute percentage error (MAPE)
19. Forecasting infant mortality rate for China: A comparison between α-sutte indicator, ARIMA, and holt-winters
20. Analysis and forecasting infant mortality rate (IMR) in Egypt until year 2000