Trend of COVID-19 network attention based on ARIMA and LSTM

doi:10.16265/j.cnki.issn 1003-3033.2020.12.006

Abstract

Abstract: In order to effectively monitor and manage online public opinion caused by COVID-19, data of public opinion were predicted and analyzed based on ARIMA model and LSTM neural network. Then, attention value of COVID-19 from network users in Wuhan and the whole country was collected by using Baidu index. Time series data were developed, and prediction models were established. Finally, parameter estimation, model diagnosis, and model evaluation were carried out for each prediction model. The results show that prodromal period, outbreak period, fluctuation period and fading period of internet public opinion are 4 days, 7 days, 14 days and 32 days respectively, and the time it takes to reach a peak is 13 days. The model can well simulate change trend of COVID-19 network public opinion attention, and prediction results of local data fitting model is better than that of national one.

Key words: auto regressive integrated moving average (ARIMA), long-short term memory (LSTM), corona virus disease 2019 (COVID-19), network public opinion, time series

CLC Number:

X928

JING Nan, HU Yi, HAN Xishuang. Trend of COVID-19 network attention based on ARIMA and LSTM[J]. China Safety Science Journal, 2020, 30(12): 37-42.

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