The usefulness of forecasting confirmed cases of the novel coronavirus with statistical and neural network models

Abstract

This study forecast Japanese confirmed cases of the novel coronavirus in order to assist in decisions. There are statistical models and machine learning models for forecasting the time series data. Statistical models performed better than machine learning models, which was confirmed by the experiment results of the comparing. Therefore, this study forecast confirmed cases of the novel coronavirus with SARIMA(Seasonal AutoRegressive Integrated Moving Average) and RNN( Recurrent neural network compares each model with RMSE (Root Mean Square Error). As the result, RNN(with vector inputs) is better than machine learning models.