Author: Zhang, Y.; Sun, J.
Title: A COVID-19 Epidemics Trend Prediction Algorithm Based on LSTM Cord-id: 1jkah8rq Document date: 2021_1_1
ID: 1jkah8rq
Snippet: The polynomial regression algorithm can fit almost all data sequences by adding high-order terms of independent variables, but on long sequences, it can only reflect the development trends of the sequences, and the predicted value is not accurate enough. RNN has great advantages in predicting and fitting time sequence data, especially Long Short-Term Memory (LSTM) network has good performance in predicting long time sequence. This paper respectively adopts polynomial regression and LSTM analysis
Document: The polynomial regression algorithm can fit almost all data sequences by adding high-order terms of independent variables, but on long sequences, it can only reflect the development trends of the sequences, and the predicted value is not accurate enough. RNN has great advantages in predicting and fitting time sequence data, especially Long Short-Term Memory (LSTM) network has good performance in predicting long time sequence. This paper respectively adopts polynomial regression and LSTM analysis and predict the effect of epidemic prevention and control policies in various countries to control the epidemic and the economic recession. The experiment results show the Covid-19 epidemics trend prediction algorithm based on LSTM has better prediction effects, and is helpful in practical life. © 2021 IEEE.
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