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COVID-19 epidemic situation population situation prediction method based on LSTM

A prediction method, a technology for COVID-19, applied in the field of deep learning, which can solve problems such as inability to ensure accuracy

Active Publication Date: 2020-10-20
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0018] Traditional statistical learning methods (such as MCMC) use probability and statistics to model the proposed problem, formula derivation and calculation, and the accuracy of prediction often depends on the quality of the modeling, so its accuracy cannot be guaranteed

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  • COVID-19 epidemic situation population situation prediction method based on LSTM
  • COVID-19 epidemic situation population situation prediction method based on LSTM
  • COVID-19 epidemic situation population situation prediction method based on LSTM

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Embodiment Construction

[0066] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0067] Wherein, the accompanying drawings are for illustrative purposes only, and represent only schematic diagrams, rather than physical drawings, and should...

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Abstract

The invention relates to a COVID-19 epidemic situation population situation prediction method based on LSTM, and belongs to the technical field of deep learning. The method comprises the following steps: S1, acquiring epidemic situation data of COVID-19; S2, predicting the population situation of the national COVID-19 epidemic situation; S3, predicting COVID-19 epidemic situations of provinces, autonomous areas and direct administration cities; and S4, predicting the population situation of COVID-19 epidemic situations in cities. Deep learning achieves excellent effects in many fields at present, LSTM has good performance in time series prediction, time characteristics and rules of data can be well mined, and the effect is better than that of a traditional non-parametric model.

Description

technical field [0001] The invention belongs to the technical field of deep learning, and relates to an LSTM-based method for predicting the situation of a new crown pneumonia epidemic group. Background technique [0002] With the continuous development of deep learning technology, many fields have gradually turned their attention to deep learning. Deep learning has shown excellent performance in dealing with nonlinear data and multidimensional data. Recurrent Neural Network (RNN) is a kind of neural network, which is suitable for solving sequence problems. The different layers of the traditional neural network are fully connected, and the neurons in the same layer are not connected to each other. In the process of sequence processing, the output of the previous stage will affect the output of the next stage. The cyclic neural network can not only accept the input of the previous layer, but also can accept the information of the neurons of the current layer at the previou...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/80G06N3/04G06N3/08
CPCG16H50/80G06N3/049G06N3/08G06N3/048G06N3/045
Inventor 张学旺李洋洋黄胜崔一辉冯家琦林金朝
Owner CHONGQING UNIV OF POSTS & TELECOMM
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