Open AccessForecasting the COVID-19 Increment Rate in DKI Jakarta Using Non-Robust STL Decomposition and SARIMA Model
Author(s) -
Rosmelina Deliani Satrisna,
Aniq Atiqi Rohmawati,
Siti Saadah
Publication year - 2021
Publication title -
international journal on information and communication technology
Language(s) - English
Resource type - Journals
ISSN - 2356-5462
DOI - 10.21108/ijoict.v7i1.554
Subject(s) - autoregressive integrated moving average , covid-19 , statistics , mean absolute percentage error , time series , decomposition , autoregressive model , china , econometrics , computer science , mathematics , mean squared error , geography , medicine , outbreak , virology , ecology , disease , archaeology , pathology , infectious disease (medical specialty) , biology
The Corona virus known as COVID-19 was first present in Wuhan, China at this time has troubled many countries and its spread is very fast and wide. Data on daily confirmed COVID-19 cases were collected from the DKI Jakarta province between early May 2020 and late January 2021. The daily increase in confirmed COVID-19 cases has a percentage of the value of increase in total cases. In this study, modeling and analysis of forecasting the increment rate in daily number of new cases COVID-19 DKI Jakarta was carried out using the Seasonal-Trend Loess (STL) Decomposition and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. STL Decomposition is a form of algorithm developed to help decompose a Time Series, and techniques considering seasonal and non-stationary observation. The results of the best forecasting accuracy are proven by STL-ARIMA, there are MAPE and MSE which only have an error value of 0.15. This proposed approach can be used for consideration for the DKI Jakarta government in making policies for handling COVID-19, as well as for the public to adhere to health protocols.