BoundAD: Boundary-Aware Negative Generation for Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2603.18111v1
• Date: Wed, 18 Mar 2026 13:50:59 GMT
• Title: BoundAD: Boundary-Aware Negative Generation for Time Series Anomaly Detection
• Abstract summary: We propose a reconstruction-driven boundary negative generation framework that automatically constructs hard negatives through the reconstruction process of normal samples.<n>We show that the proposed method effectively improves anomaly representation learning and achieves competitive detection performance on the current dataset.
• Score: 12.167081924571951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive learning methods for time series anomaly detection (TSAD) heavily depend on the quality of negative sample construction. However, existing strategies based on random perturbations or pseudo-anomaly injection often struggle to simultaneously preserve temporal semantic consistency and provide effective decision-boundary supervision. Most existing methods rely on prior anomaly injection, while overlooking the potential of generating hard negatives near the data manifold boundary directly from normal samples themselves. To address this issue, we propose a reconstruction-driven boundary negative generation framework that automatically constructs hard negatives through the reconstruction process of normal samples. Specifically, the method first employs a reconstruction network to capture normal temporal patterns, and then introduces a reinforcement learning strategy to adaptively adjust the optimization update magnitude according to the current reconstruction state. In this way, boundary-shifted samples close to the normal data manifold can be induced along the reconstruction trajectory and further used for subsequent contrastive representation learning. Unlike existing methods that depend on explicit anomaly injection, the proposed framework does not require predefined anomaly patterns, but instead mines more challenging boundary negatives from the model's own learning dynamics. Experimental results show that the proposed method effectively improves anomaly representation learning and achieves competitive detection performance on the current dataset.

Related papers

• Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning [9.53248032827498]
  In large-scale industrial applications, labeling time series data is often prohibitively expensive and time-consuming.<n>We propose a novel framework that leverages active learning to iteratively enhance the performance of unsupervised models.<n>Our framework's core contributions are (1) a masked time-series reconstruction feedback strategy that forces the model to learn robust temporal dependencies, and (2) a minimax learning strategy that promotes robustness by differentially treating normal and abnormal samples.
  arXiv  Detail & Related papers  (2026-07-01T10:08:49Z)
• Rethinking Structural Anomaly Detection: From Decision Boundaries to Projection Operators [51.771100367755544]
  We introduce a geometric perspective to address the mismatch between the inductive bias of existing methods and the structure of the data.<n>Specifically, we learn a projection operator onto the manifold of normal samples and define a sample as anomalous if it is altered by this projection.<n> Notably, it provides a unifying interpretation of reconstruction-based methods by explaining their success and failure in terms of projection quality.
  arXiv  Detail & Related papers  (2026-06-13T12:37:20Z)
• TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection [15.271061413934332]
  Two-stage pseudo anomaly-guided anomaly detection method is proposed.<n>Method generates pseudo-anomalous windows near the normal boundary.<n>It learns anomaly-sensitive representations through contrastive learning between normal and pseudo-anomalous windows.
  arXiv  Detail & Related papers  (2026-06-02T15:39:10Z)
• Learning Unified Representations of Normalcy for Time Series Anomaly Detection [0.0]
  In this paper, we present a novel framework, Unified Unsupervised Anomaly Detection ($textU2textAD$)<n>Our approach learns the underlying data distribution of normal samples by utilizing score-based generative modeling.<n>Our experimental evaluations demonstrate that $textU2textAD$ not only outperforms current state-of-the-art methods in detection accuracy but also identifies anomalies at significantly earlier stages of their occurrence.
  arXiv  Detail & Related papers  (2026-05-10T18:12:22Z)
• Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy [33.68487894996624]
  Time series anomaly detection (TSAD) is a critical task, but developing models that generalize to unseen data remains a major challenge.<n>We introduce textttTimeRCD, a novel foundation model for TSAD built upon a new pre-training paradigm: Relative Context Discrepancy (RCD)<n>We show that textttTimeRCD significantly outperforms existing general-purpose and anomaly-specific foundation models in zero-shot TSAD.
  arXiv  Detail & Related papers  (2025-09-25T14:05:15Z)
• One-for-More: Continual Diffusion Model for Anomaly Detection [63.50488826645681]
  Anomaly detection methods utilize diffusion models to generate or reconstruct normal samples when given arbitrary anomaly images.<n>Our study found that the diffusion model suffers from severe faithfulness hallucination'' and catastrophic forgetting''<n>We propose a continual diffusion model that uses gradient projection to achieve stable continual learning.
  arXiv  Detail & Related papers  (2025-02-27T07:47:27Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• 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)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• 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.