2019년 컴퓨터종합학술대회
Current Result Document :
| 한글제목(Korean Title) |
딥러닝을 활용한 시계열 자료의 이상치 탐지 모델에 대한 조사 |
| 영문제목(English Title) |
A Survey on Deep Learning-based Anomaly Detection Models for Time Series Data |
| 저자(Author) |
우요
강준혁
이재길
Yu Yao
Junhyeok Kang
Jae-Gil Lee
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| 원문수록처(Citation) |
VOL 46 NO. 01 PP. 0919 ~ 0921 (2019. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Anomaly detection for time series data has been of great interest to diverse research and application domains for decades. However, analysis of time series data is still challenging due to its high-dimensionality, non-station arity characteristics. In recent years, deep learning has shown its capability of dealing with high-dimensional and complex data. Since that, deep learning-based anomaly detection approaches have become increasingly popular. In this survey, we briefly introduce several kinds of deep anomaly detection models: prediction-based, reconstruc tion-based, hybrid and generative model, which provides an insight into the current work in anomaly detection for time series data. |
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| 파일첨부 |
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