About: COVID-19 growth prediction using multivariate long short term memory

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	COVID-19 growth prediction using multivariate long short term memory
Creator	Files, Journal Of L A T E X Class,
source	ArXiv
abstract	Coronavirus disease (COVID-19) spread forecasting is an important task to track the growth of the pandemic. Existing predictions are merely based on qualitative analyses and mathematical modeling. The use of available big data with machine learning is still limited in COVID-19 growth prediction even though the availability of data is abundance. To make use of big data in the prediction using deep learning, we use long short-term memory (LSTM) method to learn the correlation of COVID-19 growth over time. The structure of an LSTM layer is searched heuristically until the best validation score is achieved. First, we trained training data containing confirmed cases from around the globe. We achieved favorable performance compared with that of the recurrent neural network (RNN) method with a comparable low validation error. The evaluation is conducted based on graph visualization and root mean squared error (RMSE). We found that it is not easy to achieve the same quantity of confirmed cases over time. However, LSTM provide a similar pattern between the actual cases and prediction. In the future, our proposed prediction can be used for anticipating forthcoming pandemics. The code is provided here: https://github.com/cbasemaster/lstmcorona
has issue date	2020-05-10(xsd:dateTime)
has license	arxiv
sha1sum (hex)	7877d1209e134ea8429c0411567012aef23f70e1
resource representing a document's title	COVID-19 growth prediction using multivariate long short term memory
resource representing a document's body	covid:7877d1209e134ea8429c0411567012aef23f70e1#body_text
is schema:about of	named entity 'globe' named entity 'forecasting' named entity 'confirmed' named entity 'COVID-19' named entity 'validation' »more»

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