DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Jeon, Jooyoung | - |
dc.contributor.advisor | 전주영 | - |
dc.contributor.author | Kim, Hokyun | - |
dc.date.accessioned | 2023-06-22T19:32:11Z | - |
dc.date.available | 2023-06-22T19:32:11Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1032375&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/308362 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 미래전략대학원프로그램, 2023.2,[iv, 59 p. :] | - |
dc.description.abstract | Air pollutants are the known cause of many respiratory and cardiovascular diseases, thus being detrimental influence on human health worldwide. On top of inflicting public health, air pollutants are known to impact the economy and the policy-making process. Therefore, making accurate forecasts of air pollutants is an important matter for all sectors. We conduct probabilistic forecasts instead of point forecasts, as probabilistic forecasts allow us to prepare for events with low probability. We test probabilistic forecasting models based on traditional time series models, namely ARIMA-GARCH against the deep neural network-based time series model DeepAR. As previous research has investigated the correlation between air pollutants and other variables such as weather or traffic volume, we incorporate such covariates to our DeepAR model expecting the covariates to raise model performance. Are results show that DeepAR consistently outputs better performance for probabilistic forecasts in air pollutants compared to ARIMA-GARCH models in most cases, while incorporating covariates to DeepAR further enhances model performance for the accordingly relevant air pollutants. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Air pollutants▼aProbabilistic forecasting▼aARIMA-GARCH▼aDeepAR▼aTraffic volume | - |
dc.subject | 공기오염수치▼a확률분포적 예측▼a시계열 예측▼a딥러닝▼a교통량 데이터 | - |
dc.title | Probabilistic forecasting for air pollutants in Seoul | - |
dc.title.alternative | 서울 미세먼지 수치의 확률분포적 예측 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :미래전략대학원프로그램, | - |
dc.contributor.alternativeauthor | 김호균 | - |
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