시계열 모형과 빅데이터 분석기법을 이용한 코로나 확진자 수 예측Prediction of COVID-19 Confirmed Cases by Using Big Data and Time Series Analysis*
- Other Titles
- Prediction of COVID-19 Confirmed Cases by Using Big Data and Time Series Analysis*
- Authors
- 신동렬; 채가영; 박민재
- Issue Date
- Dec-2022
- Publisher
- 한국신뢰성학회
- Keywords
- AdaBoost; COVID-19; Deep Learning; SARIMAX; Time Series; Vaccination Rate
- Citation
- 신뢰성 응용연구, v.22, no.4, pp.352 - 362
- Journal Title
- 신뢰성 응용연구
- Volume
- 22
- Number
- 4
- Start Page
- 352
- End Page
- 362
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/30654
- DOI
- 10.33162/JAR.2022.12.22.4.352
- ISSN
- 1738-9895
- 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%.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Business Administration > Business Administration Major > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/30654)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.