Forecasting COVID-19 Confirmed Cases Using Time Series Analysis
Keywords:
COVID-19, predictive analytics, machine learning, regression, time seriesAbstract
The novel coronavirus (COVID-19) pandemic is a major global health threat that is spreading very fast around the world. In the current study, we present a new forecasting model to estimate the number of confirmed cases of COVID-19 in the next two weeks based on the previously confirmed cases recorded for 62 countries around the world. The cumulative cases of these countries represent about 95% of the total global up to the date of data gathering. Seven regression models have been used for three rounds of predictions based on the data collected between February 21, 2020 and December 29, 2020. A number of different time series features have generated using feature-engineering methods to convert a time series forecast into a supervised learning problem and then build regression models. The performance of the models was evaluated using root mean squared log error, root mean squared error, mean absolute error, mean absolute percentage error, coefficient of determination and running time. The findings show a good performance and can reduce the error about 72% with a high coefficient of R2 = 0.990. In particular, XGB and Random Forest models have demonstrated their efficiency over other models.
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