Publication:
Comparative Analysis of Approaches to Prediction of Quantitative Parameters during a Pandemic

dc.contributor.authorKolomensiy, V.
dc.contributor.authorFirsov, G.
dc.date.accessioned2024-11-29T15:26:09Z
dc.date.available2024-11-29T15:26:09Z
dc.date.issued2021
dc.description.abstract© 2021 IEEE.The purpose of this paper is to study and compare several approaches to predict quantitative parameters of an epidemiological situation. These parameters change in time is not stochastic and chaotic. For instance, the number of total infection cases increases exponentially in the beginning but tends to have a linear trend later. Such processes can be modeled in a variety of ways, for example, with the SEIR model or its modifications. This paper also compares time series models, like exponential smoothing, autoregressive models, and a neural network in application to the target task. This article describes a result of a comparison of these algorithms, and an explanation of obtained results, for instance how some characteristics of target features describe a more accurate prediction of future values by the modified SEIR model, rather than an exponential smoothing process or Holt-Winters method.
dc.format.extentС. 464-467
dc.identifier.citationKolomensiy, V. Comparative Analysis of Approaches to Prediction of Quantitative Parameters during a Pandemic / Kolomensiy, V., Firsov, G. // Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021. - 2021. - P. 464-467. - 10.1109/ElConRus51938.2021.9396350
dc.identifier.doi10.1109/ElConRus51938.2021.9396350
dc.identifier.urihttps://www.doi.org/10.1109/ElConRus51938.2021.9396350
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85104737869&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000669709800104
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/24008
dc.relation.ispartofProceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021
dc.titleComparative Analysis of Approaches to Prediction of Quantitative Parameters during a Pandemic
dc.typeConference Paper
dspace.entity.typePublication
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relation.isOrgUnitOfPublication.latestForDiscovery010157d0-1f75-46b2-ab5b-712e3424b4f5
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