부산대학교 도서관

COVID-19 has infected more than 68 million people worldwide since it was first detected about a year ago. Machine learning time series models have been implemented to forecast COVID-19 infections. In this paper, we develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. The dataset set includes the one-year cumulative COVID-19 cases between 22/01/2020 to 22/01/2021. We developed different models for the countries under study based on the spatial distribution of the infection data. Our experimental results show that the developed models can forecast COVID-19 infections with high precision.
Comment: 9 pages, Proceedings of the 13th International Conference on Computer Modeling and Simulation, ICCMS 2021, Autoregressive integrated moving average, ARIMA, Coronavirus, COVID-19, Damped Trend, Holt Linear Trend, Machine learning, Pandemic, Time series