Long Short-Term Memory Prediction for COVID19 Time Series

Abstract

Entire world has been dealing with the number of new Coronavirus 2 or COVID-19 cases. The spread of this severe acute respiratory syndrome has produced many concerns worldwide. Having data related to coronavirus available for tests, novel models for forecasting the number of new cases can be developed. In this paper, a long short-term memory (LSTM) based methodology is applied for such prediction. Here, experimental analysis is performed with the parameters, such as the number of layers and units of the network. The root mean squared error is calculated for data corresponding to the Republic of Serbia, as well as per different continents. The results show that LSTM model can be useful for further analysis and time series prediction.

Keywords

• prediction
• neural network
• lstm
• acute respiratory syndrome
• covid-19
• root mean squared error