  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.
