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COVID-19 has been one of the most severe diseases, causing a harsh pandemicall over the world, since December 2019. The aim of this study is to evaluatethe value of Long Short-Term Memory (LSTM) Networks in forecasting the totalnumber of COVID-19 cases in Turkey. The COVID-19 data for 30 days, betweenMarch 24 and April 23, 2020, are used to estimate the next fifteen days. Themean absolute error of the LSTM Network for 15 days estimation is1,69$\pm$1.35%. Whereas, for the same data, the error of the Box-Jenkins methodis 3.24$\pm$1.56%, Prophet method is 6.88$\pm$4.96% and Holt-Winters Additivemethod with Damped Trend is 0.47$\pm$0.28%. Additionally, when the number ofdeaths data is also provided with the number of total cases to the input ofLSTM Network, the mean error reduces to 0.99$\pm$0.51%. Consequently, additionof the number of deaths data to the input, results a lower error inforecasting, compared to using only the number of total cases as the input.However, Holt-Winters Additive method with Damped Trend gives superior resultsto LSTM Networks in forecasting the total number of COVID-19 cases.

Short-Term Forecasting COVID-19 Cases In Turkey Using Long Short-Term Memory Network