论文标题
使用长期短期存储网络，土耳其中的短期预测Covid-19案例
Short-Term Forecasting COVID-19 Cases In Turkey Using Long Short-Term Memory Network
论文作者
论文摘要
Covid-19一直是最严重的疾病之一，自2019年12月以来引起了全世界的严酷大流行。这项研究的目的是评估长期记忆（LSTM）网络在预测土耳其Covid-19案例总数中的价值。在2020年3月24日至4月23日之间的30天的Covid-19数据被用来估计接下来的15天。 LSTM网络在15天估算中的平均绝对错误为1,69 $ \ pm $ 1.35％。鉴于，对于相同的数据，盒子jenkins方法的错误为3.24 $ \ pm $ 1.56％，先知方法为6.88 $ \ pm $ 4.96％，而霍尔特·韦特斯（Holt-Winters）添加剂的添加剂趋势为0.47 $ \ pm $ 0.28％。此外，当还提供了LSTM网络输入的总案例数量的死亡数据数量时，平均误差将减少到0.99 $ \ pm $ 0.51％。因此，与仅使用总案例数量作为输入相比，将死亡数据的数量添加到输入中，预测误差较低。但是，在预测COVID-19案例总数方面，具有阻尼趋势的Holt-Winters添加剂为LSTM网络提供了较好的结果。
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.
