{"type":"ScholarlyArticle","title":"Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection","authors":[{"name":"Chen Liu","slug":"chen-liu","given":"Chen","family":"Liu","affiliation":"Zhejiang University"},{"name":"Shibo He","slug":"shibo-he","given":"Shibo","family":"He","affiliation":"Zhejiang University"},{"name":"Qihang Zhou","slug":"qihang-zhou","given":"Qihang","family":"Zhou","affiliation":"Zhejiang University"},{"name":"Shizhong Li","slug":"shizhong-li","given":"Shizhong","family":"Li","affiliation":"Zhejiang University"},{"name":"Wenchao Meng","slug":"wenchao-meng","given":"Wenchao","family":"Meng","affiliation":"Zhejiang University"}],"abstract":"Self-supervised methods have gained prominence in time series anomaly detection due to the scarcity of available annotations. Nevertheless, they typically demand extensive training data to acquire a generalizable representation map, which conflicts with scenarios of a few available samples, thereby limiting their performance. To overcome the limitation, we propose AnomalyLLM, a knowledge distillation-based time series anomaly detection approach where the student network is trained to mimic the features of the large language model (LLM)-based teacher network that is pretrained on large-scale datasets. During the testing phase, anomalies are detected when the discrepancy between the features of the teacher and student networks is large. To circumvent the student network from learning the teacher network’s feature of anomalous samples, we devise two key strategies. 1) Prototypical signals are incorporated into the student network to consolidate the normal feature extraction. 2) We use synthetic anomalies to enlarge the representation gap between the two networks. AnomalyLLM demonstrates state-of-the-art performance on 15 datasets, improving accuracy by at least 14.5% in the UCR dataset.","doi":"10.24963/ijcai.2024/239","url":"https://ijcai.org/proceedings/2024/239","pdf":"https://ijcai.org/proceedings/2024/0239.pdf","markdown":"https://ijcai.org/proceedings/2024/239.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/239","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":239,"pages":{"first":2162,"last":2170},"keywords":["Machine Learning: ML: Unsupervised learning","Data Mining: DM: Mining spatial and/or temporal data","Data Mining: DM: Anomaly/outlier detection"],"videos":[],"references":[{"position":1,"raw":"[Audibert et al., 2020] Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, and Maria A Zuluaga. 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