Coronavirus Epidemic (COVID-19) Prediction and Trend Analysis Based on Time Series

Liu, Z.; Zuo, J.; Lv, R.; Liu, S.; Wang, W.

In the global fight against the novel corona-virus pneumonia epidemic (COVID-19), a reasonable prediction of the spread of the epidemic has important reference significance for epidemic prevention and control. In order to solve the problem of time series prediction and analysis of the epidemic with limited sample data, nonlinear and high-dimensional features, this study applies the Nonlinear Auto-Regressive neural network (NAR) model for machine learning. The paper predicts the development of the epidemic in the two dimensions of the number of confirmed cases and the number of deaths in major countries in the world, and compares NAR with the traditional Logistic Regression (LR), the classic time series model ARIMA and the SEIR infectious disease dynamic model. This research provides rapid decision-making and new ideas for countries to respond to the 'post-epidemic era'. © 2021 IEEE.