DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2306.10347v2
• Date: Wed, 11 Oct 2023 07:50:09 GMT
• Title: DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
• Authors: Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen, Liang Sun
• Abstract summary: 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. We propose DCdetector, a multi-scale dual attention contrastive representation learning model.
• Score: 26.042898544127503
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• USD: Unsupervised Soft Contrastive Learning for Fault Detection in Multivariate Time Series [6.055410677780381]
  We introduce a combination of data augmentation and soft contrastive learning, specifically designed to capture the multifaceted nature of state behaviors more accurately. This dual strategy significantly boosts the model's ability to distinguish between normal and abnormal states, leading to a marked improvement in fault detection performance across multiple datasets and settings.
  arXiv  Detail & Related papers  (2024-05-25T14:48:04Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection [53.83593870825628]
  One main challenge in time series anomaly detection (TSAD) is the lack of labelled data in many real-life scenarios. Most of the existing anomaly detection methods focus on learning the normal behaviour of unlabelled time series in an unsupervised manner. We introduce a novel end-to-end self-supervised ContrAstive Representation Learning approach for time series anomaly detection.
  arXiv  Detail & Related papers  (2023-08-18T04:45:56Z)
• Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection [12.719327447589345]
  A sketched multi-view subspace learning model is proposed for anomalous change detection. The proposed model preserves major information from the image pairs and improves computational complexity. experiments are conducted on a benchmark hyperspectral remote sensing dataset and a natural hyperspectral dataset.
  arXiv  Detail & Related papers  (2022-10-09T14:08:17Z)
• Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors [50.6434162489902]
  We propose a reconstruction-based anomaly detection method, MissGAN, iteratively learning to decode and encode naturally smooth time series. MissGAN does not need labels or only needs labels of normal instances, making it widely applicable.
  arXiv  Detail & Related papers  (2022-04-18T04:34:15Z)
• CutPaste: Self-Supervised Learning for Anomaly Detection and Localization [59.719925639875036]
  We propose a framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects.
  arXiv  Detail & Related papers  (2021-04-08T19:04:55Z)
• Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images [23.79184121052212]
  Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images. We propose a novel self-supervised representation learning method, called Constrained Contrastive Distribution learning for anomaly detection (CCD) Our method outperforms current state-of-the-art UAD approaches on three different colonoscopy and fundus screening datasets.
  arXiv  Detail & Related papers  (2021-03-05T01:56:58Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.