The LSTM Based Trend Prediction Model: A Case Study of Beijing COVID-19 Epidemic Data
- 10.2991/978-2-494069-31-2_82How to use a DOI?
- COVID-19; Data Analysis; LSTM; Prediction Model
The paper tries to build a COVID-19 trend prediction model with data mining methods and deep learning algorithm by taking Beijing COVID-19 epidemic data as an example. Currently, the Corona Virus Disease 2019 (COVID-19) is spreading around the world, posing a huge threat to the human society. By studying the epidemic curve, the overseas imported cases and incidence rate in different age groups, we draw the conclusion that the overseas imported cases are the main reason for the second rise of the cumulative confirmed cases curve and the incidence rate of the older people over 60 in Beijing is the highest. To support the prevention and control of the COVID-19, we extract the daily cumulative confirmed cases in Beijing from January 20th, 2020 to April 26th, 2020 to establish a trend prediction model based on LSTM method (Long Short-Term Memory). And we compared the proposed LSTM based prediction model with the Support Vector Regression (SVR) based prediction model and the Autoregressive Integrated Moving Average (ARIMA) based prediction model. The result shows the effectiveness of the proposed model.
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Cite this article
TY - CONF AU - Ruiling Zhao AU - Linchao Yang PY - 2022 DA - 2022/12/29 TI - The LSTM Based Trend Prediction Model: A Case Study of Beijing COVID-19 Epidemic Data BT - Proceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022) PB - Atlantis Press SP - 672 EP - 681 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-494069-31-2_82 DO - 10.2991/978-2-494069-31-2_82 ID - Zhao2022 ER -