{"type":"ScholarlyArticle","title":"Perturbation Guiding Contrastive Representation Learning for Time Series Anomaly Detection","authors":[{"name":"Liaoyuan Tang","slug":"liaoyuan-tang","given":"Liaoyuan","family":"Tang","affiliation":"School of Artificial Intelligence, Optics and Electronics (iOPEN) , Northwestern Polytechnical University , Xi'an , China"},{"name":"Zheng Wang","slug":"zheng-wang","given":"Zheng","family":"Wang","affiliation":"School of Artificial Intelligence, Optics and Electronics (iOPEN) , Northwestern Polytechnical University , Xi'an , China"},{"name":"Guanxiong He","slug":"guanxiong-he","given":"Guanxiong","family":"He","affiliation":"School of Artificial Intelligence, Optics and Electronics (iOPEN) , Northwestern Polytechnical University , Xi'an , China"},{"name":"Rong Wang","slug":"rong-wang","given":"Rong","family":"Wang","affiliation":"School of Artificial Intelligence, Optics and Electronics (iOPEN) , Northwestern Polytechnical University , Xi'an , China"},{"name":"Feiping Nie","slug":"feiping-nie","given":"Feiping","family":"Nie","affiliation":"School of Artificial Intelligence, Optics and Electronics (iOPEN) , Northwestern Polytechnical University , Xi'an , China"}],"abstract":"Time series anomaly detection is a critical task with applications in various domains. Due to annotation challenges, self-supervised methods have become the mainstream approach\r\nfor time series anomaly detection in recent years. However,\r\ncurrent contrastive methods categorize data perturbations into\r\nbinary classes, normal or anomaly, which lack clarity on the specific impact of different perturbation methods. Inspired by the hypothesis that \"the higher the probability of misclassifying perturbation types, the higher the probability of anomalies\", we propose PCRTA,  our\r\napproach firstly devises a perturbation classifier to learn the\r\npseudo-labels of data perturbations. Furthermore, for addressing \"class collapse issue\" in contrastive learning, we propose a perturbation guiding positive and negative samples\r\nselection strategy by introducing learnable perturbation classification networks. Extensive experiments on six realworld datasets demonstrate the significant superiority of our\r\nmodel over thirteen state-of-the-art competitors, and obtains average\r\n5.14%, 8.24% improvement in F1 score and AUC-PR, respectively.","doi":"10.24963/ijcai.2024/548","url":"https://ijcai.org/proceedings/2024/548","pdf":"https://ijcai.org/proceedings/2024/0548.pdf","markdown":"https://ijcai.org/proceedings/2024/548.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/548","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":"Machine Learning","track":"Main Track","number":548,"pages":{"first":4955,"last":4963},"keywords":["Machine Learning: ML: Representation learning","Machine Learning: ML: Time series and data streams","Machine Learning: ML: Unsupervised learning","Machine Learning: ML: Self-supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal and Aggarwal, 2017] Charu C Aggarwal and Charu C Aggarwal. 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