Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML

Abstract

The use of computational intelligence techniques is being considered for a vast number of applications not only because of its increasing popularity but also because the results achieve good performance and are promising to keep improving. In this chapter, we present the basic theoretical aspects and assumptions of the LSTM model and H20 AutoML framework. It is evaluated on the prediction of the COVID-19 epidemiological data series for five different countries (China, United States, Brazil, Italy, and Singapore), each of them with specific curves, which are results of policies and decisions during the pandemic spread. The discussion about the results is performed with the focus on three evaluation criteria: $R^2$ Score, MAE, and MSE. Higher $R^2$ Score was obtained when the sample time series was smoothly increasing or decreasing. The results obtained by the AutoML framework achieved a higher $R^2$ Score and lower MAE and MSE when compared with LSTM and also with other techniques proposed in the book, such as ARIMA and Kalman predictor. The application of machine learning algorithm selector might be a promising candidate for a good predictor for epidemic time series.