IMPLEMENTATION OF A TIME SERIES PREDICTION ALGORITHM FOR COVID-19 CONFIRMED CASES IN MALAYSIA : A PRELIMANARY STUDY AFTER THE RECOVERY MOVEMENT CONTROL ORDER - Tee, Bee Lin (Universiti Malaysia Sarawak (UNIMAS), 2020)
Time series analysis is a method to predict future values by reading the previous data in time format. It is widely used nowadays especially weather forecast, economy trading, many industries are utilizing it for their business planning, etc. Meanwhile, coronavirus disease 2019, also known as COVID-19, is the pandemic that has been researched extensively around the world. In Malaysia, the Recovery MCO (RMCO) is starting from 10th June 2020 until 31st August 2020 where certain social, educational and businesses are allowed to operate after the spread of disease starts decreasing (Loo, 2020). But there is new cluster reported on 16th July after the cases were thought decreasing gradually (Kaur, 2020). Although it is not announced as second outbreak yet it is necessary to study the time series Covid-19 cases in Malaysia to forecast the possible number of cases in future for better preparation and planning. In this research, ARIMA time series model applied to predict the Covid-19 future daily cases by adapting their time series dataset from the Johns Hopkins University official website. The data extracts Malaysia’s Covid-19 confirmed cases from 22nd January 2020 to 30th July 2020. The parameter of ARIMA (2,1,1) has the highest accuracy compared to other tested parameters with 10.4269 of RMSE value and 2.1548 of MAPE value. The result of the predicted number of Covid-19 daily cases is between 15 to 17 in fourteen days with a 95% confidence interval.