Open AccessComparison of exponential smoothing method and autoregressive integrated moving average (ARIMA) method in predicting dengue fever cases in the city of Palembang
Author(s) -
Ensiwi Munarsih,
Imelda Saluza
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1521/3/032100
Subject(s) - autoregressive integrated moving average , exponential smoothing , dengue fever , moving average , statistics , dengue virus , econometrics , time series , medicine , computer science , mathematics , virology
Forecasting is the process of making a statement about an event where the event has not been known or observed. For pharmaceutical students, learning about forecasting techniques can help determine the treatment of various diseases, one of which is dengue fever. Dengue fever is an acute disease caused by the dengue virus. Dengue fever is still a public health issue in major cities in Indonesia, one of which is Palembang. Based on the profile done by Palembang City’s Public Health Office in 2017, dengue fever cases in the area from year to year tend to fluctuates. To get the overview of the number of dengue fever cases in the upcoming years, time series forecasting methods are used, namely the Exponential Smoothing method and the Autoregressive Integrated Moving Average (ARIMA) method. Afterward, the results of predictions from the two methods are compared. Forecasting using the ARIMA method gives the smallest MSE and MAE results of 108077.877 and 172.424, respectively, compared to the Exponential Smoothing method. This means that the ARIMA method is better at predicting the number of dengue fever cases in Palembang in the coming years.