시계열 모형과 빅데이터 분석기법을 이용한 코로나 확진자 수 예측
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 신동렬 | - |
dc.contributor.author | 채가영 | - |
dc.contributor.author | 박민재 | - |
dc.date.accessioned | 2022-12-26T01:41:39Z | - |
dc.date.available | 2022-12-26T01:41:39Z | - |
dc.date.created | 2022-12-26 | - |
dc.date.issued | 2022-12 | - |
dc.identifier.issn | 1738-9895 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/30654 | - |
dc.description.abstract | Purpose: In this study, we used time series models, deep learning (DL) models, ensemble models, and other models to predict COVID-19 confirmed cases. We developed time series models with exogenous variables and achieved show promising results for the correlation between COVID-19 confirmed cases and vaccination rates. Methods: We proposed a method based on time series and deep learning for model development. The proposed method can accurately predict the number of confirmed cases of COVID-19 per day by utilizing the COVID-19 vaccination rate as an exogenous variable. Thus, improved prediction accuracy can be achieved using DL ensemble models. Results: The AdaBoost-LSTM model yielded superior results than the other time series models, and the SARIMAX(3rd vaccination rate)(2,1,3)(1,1,1,7) model exhibited better prediction performance than other time series models. Conclusion: The SARIMAX(2,1,3)(1,1,1,7) model exhibited better performance than gated recurrent unit/long short-term memory models. The use of the AdaBoost algorithm improved the prediction performance of the model by approximately 51.6%. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | 한국신뢰성학회 | - |
dc.title | 시계열 모형과 빅데이터 분석기법을 이용한 코로나 확진자 수 예측 | - |
dc.title.alternative | Prediction of COVID-19 Confirmed Cases by Using Big Data and Time Series Analysis* | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 박민재 | - |
dc.identifier.doi | 10.33162/JAR.2022.12.22.4.352 | - |
dc.identifier.bibliographicCitation | 신뢰성 응용연구, v.22, no.4, pp.352 - 362 | - |
dc.relation.isPartOf | 신뢰성 응용연구 | - |
dc.citation.title | 신뢰성 응용연구 | - |
dc.citation.volume | 22 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 352 | - |
dc.citation.endPage | 362 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002906262 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | AdaBoost | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | SARIMAX | - |
dc.subject.keywordAuthor | Time Series | - |
dc.subject.keywordAuthor | Vaccination Rate | - |
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