The widespread outbreak of Corona Virus Disease 2019 (COVID-19) poses a great risk to the lives and property of the world's citizens, especially in the USA, and Japan cases continue to increase dynamically. Several statistical models, machine learning models, and deep learning models were reported in the literature to forecast COVID-19 but there is no comprehensive report on all of them. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic wave in America and Japan (the period after 10 October 2022). The autoregressive moving average (ARIMA) model, support vector regression (SVR) model, and the long short-term memory (LSTM) model were employed to forecast the number of confirmed cases. In this study, machine learning and deep learning perform significantly better than traditional statistical models. The results show that SVR and LSTM are better single prediction models, which can help take precautions and policy formulation for this epidemic in other countries.

Jinxuan Luo, Wenxuan Xue, Jin Zhao, Peiying Liu, Chenchen Yang. Prediction of confirmed cases of COVID-19 through time series models: A comparative study. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 11: 114-123. https://doi.org/10.25236/AJCIS.2023.061115.

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