DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 최재식 | - |
dc.contributor.author | Oh, Sekwang | - |
dc.contributor.author | 오세광 | - |
dc.date.accessioned | 2024-07-25T19:30:48Z | - |
dc.date.available | 2024-07-25T19:30:48Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1045738&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/320550 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 김재철AI대학원, 2023.8,[iii, 26 p. :] | - |
dc.description.abstract | Trend filtering simplifies the complex patterns of time series data. However, smoothness of trend filtering assumes constantness with a fixed criterion for the entire sequence. Because of constantness, noise filtering occurs in sub-sequences that should be fully reflected in the trend, such as abrupt changes. In this thesis, we propose a Dynamic Trend Filtering network (DTF-net) based on simple Reinforcement Learning (RL). Our model starts with the hypothesis that noise has two types: important events as structural breakpoints and simple white noise. The goal is to include an important event in the trend that existing trend filtering algorithms remove as noise. In DTF-net, discrete action defines Dynamic Trend Points (DTP), which we define as Trend Point Detection. The trend extracted from DTPs uses flexible noise filters, thus conserving important original sub-sequences while removing noise for other sub-sequences as much as necessary. By using forecasting MSE as a reward, we can also capture the temporal dependency before and after the action event when extracting the trend. We demonstrate that DTF-net reflects important events more effectively than other trend filtering methods by analyzing synthetic data. Furthermore, the noise-robust property of DTF-net enhances forecasting performance by utilizing the trends obtained from DTF-net as an additional feature. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 시계열 데이터▼a트렌드 필터링▼a구조적 단절▼a변화 지점 감지▼a강화학습 | - |
dc.subject | Time series▼aTrend filtering▼aStructural breakpoints▼aChange point detection▼aReinforcement learning | - |
dc.title | Dynamic time series trend filtering through structural breaks detection with reinforcement learning | - |
dc.title.alternative | 강화학습 기반의 구조적 단절 감지를 통한 다이나믹 시계열 트렌드 필터링 기법 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :김재철AI대학원, | - |
dc.contributor.alternativeauthor | Choi, Jaesik | - |
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