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http://dx.doi.org/10.6109/jkiice.2022.26.2.187

Comparative analysis of performance of BI-LSTM and GRU algorithm for predicting the number of Covid-19 confirmed cases  

Kim, Jae-Ho (Department of Computer Science, The University of Suwon)
Kim, Jang-Young (Department of Computer Science, The University of Suwon)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.2, 2022 , pp. 187-192 More about this Journal
Abstract
Even the announcing date for the staring date of "With Corona" has been decided, still many people have not completed vaccination, the most important condition for starting the With Corona, because of concerns for its side effects. In addition, although the economy may can be recovered by the With Corona, but the number of infected people may can be surged. In this paper, in order to awaken the people for the awareness of Corona 19 in advance of the With Corona, the Corona 19 is predicted through a non-linear probability process. Here, among the deep learning RNN, BI-LSTM, which is a bidirectional LSTM, and GRU, gates decreased than LSTM have been used. And this has been compared and analyzed through train set, test set, loss function, residual analysis, normal distribution, and autocorrelation, and compared and predicted for which has a better performance.
Keywords
Covid-19; RNN; LSTM; BI-LSTM; GRU;
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