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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.21833 |
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| _version_ | 1866916664486920192 |
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| author | Yang, Alan Chen, Yulin Lee, Sean Montes, Venus |
| author_facet | Yang, Alan Chen, Yulin Lee, Sean Montes, Venus |
| contents | Time series anomaly detection (TSAD) is of widespread interest across many industries, including finance, healthcare, and manufacturing. Despite the development of numerous automatic methods for detecting anomalies, human oversight remains necessary to review and act upon detected anomalies, as well as verify their accuracy. We study the use of multimodal large language models (LLMs) to partially automate this process. We find that LLMs can effectively identify false alarms by integrating visual inspection of time series plots with text descriptions of the data-generating process. By leveraging the capabilities of LLMs, we aim to reduce the reliance on human effort required to maintain a TSAD system |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21833 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Refining Time Series Anomaly Detectors using Large Language Models Yang, Alan Chen, Yulin Lee, Sean Montes, Venus Computation and Language Time series anomaly detection (TSAD) is of widespread interest across many industries, including finance, healthcare, and manufacturing. Despite the development of numerous automatic methods for detecting anomalies, human oversight remains necessary to review and act upon detected anomalies, as well as verify their accuracy. We study the use of multimodal large language models (LLMs) to partially automate this process. We find that LLMs can effectively identify false alarms by integrating visual inspection of time series plots with text descriptions of the data-generating process. By leveraging the capabilities of LLMs, we aim to reduce the reliance on human effort required to maintain a TSAD system |
| title | Refining Time Series Anomaly Detectors using Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.21833 |