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Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series
[article]
2020
arXiv
pre-print
To assist policy makers in taking adequate decisions to stop the spread of COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. Materials and Methods: This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using Bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar
arXiv:2009.12325v1
fatcat:uke7qzkld5f3dkqxobzuesi77q