  New coronavirus disease (COVID-19) has constituted a global pandemic and has
spread to most countries and regions in the world. By understanding the
development trend of a regional epidemic, the epidemic can be controlled using
the development policy. The common traditional mathematical differential
equations and population prediction models have limitations for time series
population prediction, and even have large estimation errors. To address this
issue, we propose an improved method for predicting confirmed cases based on
LSTM (Long-Short Term Memory) neural network. This work compared the deviation
between the experimental results of the improved LSTM prediction model and the
digital prediction models (such as Logistic and Hill equations) with the real
data as reference. And this work uses the goodness of fitting to evaluate the
fitting effect of the improvement. Experiments show that the proposed approach
has a smaller prediction deviation and a better fitting effect. Compared with
the previous forecasting methods, the contributions of our proposed improvement
methods are mainly in the following aspects: 1) we have fully considered the
spatiotemporal characteristics of the data, rather than single standardized
data; 2) the improved parameter settings and evaluation indicators are more
accurate for fitting and forecasting. 3) we consider the impact of the epidemic
stage and conduct reasonable data processing for different stage.
