Bingjie Yan1, Jun Wang1, Zhen Zhang2, Xiangyan Tang1, *, Yize Zhou1, Guopeng Zheng1, Qi Zou1, Yao Lu1, Boyi Liu3, Wenxuan Tu4, Neal Xiong5
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1473-1490, 2020, DOI:10.32604/cmc.2020.011317
Abstract New coronavirus disease (COVID-19) has constituted a global pandemic and
has spread to most countries and regions in the world. Through understanding the
development trend of confirmed cases in a region, the government can control the
pandemic by using the corresponding policies. However, 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 compares the deviation between
the experimental results of the improved LSTM… More >
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