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http://dx.doi.org/10.6109/jkiice.2021.25.7.884

Covid19 trends predictions using time series data  

Kim, Jae-Ho (Department of Computer Science, The University of Suwon)
Kim, Jang-Young (Department of Computer Science, The University of Suwon)
Abstract
The number of people infected with Covid-19 in Korea seemed to be gradually decreasing thanks to various efforts such as social distancing and vaccines. However, just as the number of infected people increased after a particular incident on February 20, 2020, the number of infected people has been increasing rapidly since December 2020 by approximately 500 per day. Therefore, the future Covid-19 is predicted through the Prophet algorithm using Kaggle's dataset, and the explanatory power for this prediction is added through the coefficient of determination, mean absolute error, mean percent error, mean square difference, and mean square deviation through Scikit-learn. Moreover, in the absence of a specific incident rapidly increasing the cases of Covid-19, the proposed method predicts the number of infected people in Korea and emphasizes the importance of implementing epidemic prevention and quarantine rules for future diseases.
Keywords
Covid19; Prophet algorithmn; Coefficient of determination; Mean absolute error(MAE); Mean percentage error(MPE);
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