Long Short-Term Memory Forecasting for COVID19 Data

被引：0

作者：

Milivojevic, Milan S. ^{[1
]}

Gavrovska, Ana ^{[1
]}

机构：

[1] Univ Belgrade, Sch Elect Engn, Bulevar Kralja Aleksandra 73, Belgrade 11120, Serbia

来源：

2020 28TH TELECOMMUNICATIONS FORUM (TELFOR) | 2020年

关键词：

Severe Acute Respiratory Syndrome Coronavirus 2; forecasting; neural network; long short-term memory;

D O I：

10.1109/telfor51502.2020.9306601

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Nowadays everyone is talking about Coronavirus 2 or COVID-19. It is a severe acute respiratory syndrome which produces a lot of concerns around the globe. Since data is available for everyone, as well as for the Republic of Serbia, we used it for experiments via a neural network. Here, a long short-term memory approach is applied in order to make experiments with its parameters, such as the number of layers and the number of hidden units. The results show proper modelling from the standard root mean square error standpoint. The paper contribution is related to testing the parameters in the long short-term memory approach for recent data from the Republic of Serbia.

引用

页码：276 / 279

页数：4

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