Publication
COVID-19 time series prediction
| dc.contributor.author | Oliveira, Leonardo Sestrem de | |
| dc.contributor.author | Gruetzmacher, Sarah | |
| dc.contributor.author | Teixeira, João Paulo | |
| dc.date.accessioned | 2022-01-17T11:29:45Z | |
| dc.date.available | 2022-01-17T11:29:45Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | The Artificial Neural Network (ANN) is a computer technique that uses a mathematical model to represent a simpler form of the biologic neural structure. It is formed by many processing units and its intelligent behavior comes from the iterations between these units. One application of the ANN is for time series prediction algorithms, where the network learns the behavior of time dependent data and it is able to predict future values. In this work, the ANN is applied in predicting the number of COVID-19 confirmed cases and deaths and also the future seven days for the time series of Brazil, Portugal and the United States. From the simulations it is possible to conclude that the prediction of confirmed cases and deaths from COVID-19 have been successfully made by the ANN. Overall, the ANN with a specific test set had a Mean Squared Error (MSE) 50% higher than the ANN with a random test set. The combination of the sigmoidal and linear activation functions and the Levenberg-Marquardt training function had the lowest MSE for all cases | pt_PT |
| dc.description.sponsorship | This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Oliveira, Leonardo Sestrem de; Gruetzmacher, Sarah Beatriz; Teixeira, João Paulo (2021). COVID-19 time series prediction. In International Conference on ENTERprise Information Systems (CENTERIS), International Conference on Project MANagement (ProjMAN), International Conference on Health and Social Care Information Systems and Technologies (HCist). Procedia Computer Science. ISSN 1877-0509. p. 973-980. | pt_PT |
| dc.identifier.doi | 10.1016/j.procs.2021.01.254 | pt_PT |
| dc.identifier.issn | 1877-0509 | |
| dc.identifier.uri | http://hdl.handle.net/10198/24672 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Brazil covid 19 prediction | pt_PT |
| dc.subject | Covid 19 prediction | pt_PT |
| dc.subject | Portugal covid 19 prediction | pt_PT |
| dc.subject | Time series | pt_PT |
| dc.subject | USA covid 19 prediction | pt_PT |
| dc.title | COVID-19 time series prediction | pt_PT |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT | |
| oaire.citation.endPage | 980 | pt_PT |
| oaire.citation.startPage | 973 | pt_PT |
| oaire.citation.title | Procedia Computer Science | pt_PT |
| oaire.citation.volume | 181 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Oliveira | |
| person.familyName | Gruetzmacher | |
| person.familyName | Teixeira | |
| person.givenName | Leonardo Sestrem de | |
| person.givenName | Sarah | |
| person.givenName | João Paulo | |
| person.identifier | 663194 | |
| person.identifier.ciencia-id | 6F18-DAD8-ACDC | |
| person.identifier.ciencia-id | 4F15-B322-59B4 | |
| person.identifier.orcid | 0000-0002-9344-3075 | |
| person.identifier.orcid | 0000-0002-1193-4461 | |
| person.identifier.orcid | 0000-0002-6679-5702 | |
| person.identifier.rid | N-6576-2013 | |
| person.identifier.scopus-author-id | 57216177941 | |
| person.identifier.scopus-author-id | 57069567500 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
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