About: An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM
Creator	Wang, Jun Lu, Yao Zou, Qi Liu, Boyi Tang, Xiangyan Tu, Wenxuan Xiong, Naixue Yan, Bingjie Zheng, Guopeng Zhou, Yize
source	ArXiv
abstract	New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. By understanding the development trend of a regional epidemic, the epidemic can be controlled using the development policy. 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 compared 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. And 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.
has issue date	2020-05-05(xsd:dateTime)
has license	arxiv
sha1sum (hex)	f4b154b35cca099757f91d36bf9522107fa35331
resource representing a document's title	An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM
resource representing a document's body	covid:f4b154b35cca099757f91d36bf9522107fa35331#body_text
is schema:about of	named entity 'control' named entity 'cases' named entity 'Cases' named entity 'USING' named entity 'HILL' named entity 'GLOBAL' named entity 'COVID-19' named entity 'NETWORK' named entity 'CASES' named entity 'METHOD' named entity 'PLAYS' named entity 'DIFFERENTIAL EQUATIONS' named entity 'PROPOSED' named entity 'IS A' named entity 'DAE' named entity 'POLICIES' named entity 'SHENG' named entity 'CAPTURE' named entity 'DIFFERENT' named entity 'EXPERIMENTAL RESULTS' named entity 'MATHEMATICAL' named entity 'IMPACT' named entity 'DEVIATION' named entity 'SET' named entity 'PREDICTION' named entity 'TREND' named entity 'DYNAMICS' named entity 'COMPARED' named entity 'RESEARCH' named entity 'FORECASTING' named entity 'UNDERSTANDING' named entity 'SHORT TERM MEMORY' named entity 'NEW' named entity 'REFERENCE' named entity 'LSTM' named entity 'POPULATION' named entity 'address' named entity 'fitting' named entity 'evaluate' named entity 'data' named entity 'settings' named entity 'network' named entity 'time series' named entity 'effective' named entity 'experiments' named entity 'sequence' named entity 'real' named entity 'machine learning' named entity 'accurate' named entity 'indicators' named entity 'LSTM' named entity 'improved' named entity 'mathematical' named entity 'Logistic' named entity 'model' named entity 'fitting' named entity 'impact' named entity 'population' named entity 'disease' named entity 'neural network' named entity 'time series prediction' named entity 'RNN' named entity 'R. M.' named entity 'decision-making' named entity 'LSTM' named entity 'LSTM' named entity 'COVID-19' named entity 'time series prediction' named entity 'learning rate' named entity 'China' named entity 'epidemic' named entity 'prevention and control' named entity 'tanh' named entity 'training data' named entity 'loss function' named entity 'time series prediction' named entity 'time series' named entity 'LSTM'

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