{
  "id": "2306.10347",
  "version": "v1",
  "published": "2023-06-17T13:40:15.000Z",
  "updated": "2023-06-17T13:40:15.000Z",
  "title": "DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection",
  "authors": [
    "Yiyuan Yang",
    "Chaoli Zhang",
    "Tian Zhou",
    "Qingsong Wen",
    "Liang Sun"
  ],
  "categories": [
    "cs.LG",
    "cs.AI"
  ],
  "abstract": "Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundamental challenge for this task is to learn a representation map that enables effective discrimination of anomalies. Reconstruction-based methods still dominate, but the representation learning with anomalies might hurt the performance with its large abnormal loss. On the other hand, contrastive learning aims to find a representation that can clearly distinguish any instance from the others, which can bring a more natural and promising representation for time series anomaly detection. In this paper, we propose DCdetector, a multi-scale dual attention contrastive representation learning model. DCdetector utilizes a novel dual attention asymmetric design to create the permutated environment and pure contrastive loss to guide the learning process, thus learning a permutation invariant representation with superior discrimination abilities. Extensive experiments show that DCdetector achieves state-of-the-art results on multiple time series anomaly detection benchmark datasets. Code is publicly available at https://github.com/DAMO-DI-ML/KDD2023-DCdetector.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-06-17T13:40:15.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "time series anomaly detection",
      "dual attention contrastive representation learning",
      "dual attention asymmetric design"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}