id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-124618-51235tn2	Said, Ahmed Ben	Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series	2020-09-10		.txt	text/plain	2571	174	58	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 demographic and socioeconomic aspects and health sector indicators using K-Means clustering algorithm. The cumulative cases data for each clustered countries enriched with data related to the lockdown measures are fed to the Bidirectional LSTM to train the forecasting model. Conclusion: Using data of multiple countries in addition to lockdown measures improve accuracy of the forecast of daily cumulative COVID-19 cases. Our contribution consists of first grouping countries having similar demographic and socio-economic properties and health sector indicators then using COVID-19 data from each cluster to build the prediction model. The multivariate time series is used to train a deep learning Bi-LSTM network to forecast future cumulative number of cases.	./cache/cord-124618-51235tn2.txt	./txt/cord-124618-51235tn2.txt