  COVID-19 has been one of the most severe diseases, causing a harsh pandemic
all over the world, since December 2019. The aim of this study is to evaluate
the value of Long Short-Term Memory (LSTM) Networks in forecasting the total
number of COVID-19 cases in Turkey. The COVID-19 data for 30 days, between
March 24 and April 23, 2020, are used to estimate the next fifteen days. The
mean absolute error of the LSTM Network for 15 days estimation is
1,69$\pm$1.35%. Whereas, for the same data, the error of the Box-Jenkins method
is 3.24$\pm$1.56%, Prophet method is 6.88$\pm$4.96% and Holt-Winters Additive
method with Damped Trend is 0.47$\pm$0.28%. Additionally, when the number of
deaths data is also provided with the number of total cases to the input of
LSTM Network, the mean error reduces to 0.99$\pm$0.51%. Consequently, addition
of the number of deaths data to the input, results a lower error in
forecasting, compared to using only the number of total cases as the input.
However, Holt-Winters Additive method with Damped Trend gives superior results
to LSTM Networks in forecasting the total number of COVID-19 cases.
