@Article{cmc.2020.011317,
AUTHOR = {Bingjie Yan, Jun Wang, Zhen Zhang, Xiangyan Tang, Yize Zhou, Guopeng Zheng, Qi Zou, Yao Lu, Boyi Liu, Wenxuan Tu, Neal Xiong},
TITLE = {An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1473--1490},
URL = {http://www.techscience.com/cmc/v64n3/39440},
ISSN = {1546-2226},
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 prediction model and the digital prediction
models (such as Logistic and Hill equations) with the real data as reference. Furthermore,
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.},
DOI = {10.32604/cmc.2020.011317}
}