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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2025.6
- 수록면
- 2,797 - 2,801 (5page)
이용수
초록· 키워드
This paper proposes a novel approach for detecting anomalies in time series data with cyclic irregularities using a multimodal large language model. While previous methods utilizing image- based representations of time series have shown effectiveness in detecting point-wise and trend- based anomalies, they exhibit clear limitations in identifying structural deviations from periodicity, known as cyclic anomalies.
To address this limitation, we convert time series data into multimodal inputs consisting of plot images and spectrograms generated via Short-Time Fourier Transform. Additionally, we enhance the model's in- context learning capabilities by retrieving similar past time series from a database using Dynamic Time Warping and presenting them as few-shot examples.
Experimental results demonstrate that our approach significantly outperforms both zero-shot and randomly constructed few-shot baselines, as well as traditional time series anomaly detection algorithms. This study empirically shows that a multimodal large language model can serve as an effective tool for anomaly detection in time series data, particularly for detecting complex cyclic anomalies.
상세정보 수정요청해당 페이지 내 제목·저자·목차·페이지To address this limitation, we convert time series data into multimodal inputs consisting of plot images and spectrograms generated via Short-Time Fourier Transform. Additionally, we enhance the model's in- context learning capabilities by retrieving similar past time series from a database using Dynamic Time Warping and presenting them as few-shot examples.
Experimental results demonstrate that our approach significantly outperforms both zero-shot and randomly constructed few-shot baselines, as well as traditional time series anomaly detection algorithms. This study empirically shows that a multimodal large language model can serve as an effective tool for anomaly detection in time series data, particularly for detecting complex cyclic anomalies.
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목차
- Abstract
- I. 서론
- II. 관련 연구
- Ⅲ. 제안 방법
- Ⅳ. 실험
- V. 결론
- 참고문헌
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-151-25-02-093763242
