BoundAD: Boundary-Aware Negative Generation for Time Series Anomaly Detection
arXiv:2603.18111v1 Announce Type: new 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 t
arXiv:2603.18111v1 Announce Type: new 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.
Executive Summary
The article proposes a novel time series anomaly detection (TSAD) method, BoundAD, which utilizes a reconstruction-driven boundary negative generation framework to automatically construct hard negatives from normal samples. This approach improves anomaly representation learning and achieves competitive detection performance without requiring predefined anomaly patterns. The method combines a reconstruction network and a reinforcement learning strategy to induce boundary-shifted samples along the reconstruction trajectory. Experimental results demonstrate the effectiveness of BoundAD in improving anomaly detection performance on various datasets.
Key Points
- ▸ BoundAD proposes a reconstruction-driven boundary negative generation framework for TSAD.
- ▸ The method automatically constructs hard negatives from normal samples without predefined anomaly patterns.
- ▸ BoundAD combines a reconstruction network and a reinforcement learning strategy to induce boundary-shifted samples.
Merits
Strength in Anomaly Representation Learning
BoundAD effectively improves anomaly representation learning by generating challenging boundary negatives from normal samples, leading to better detection performance.
Flexibility and Adaptability
The method does not require predefined anomaly patterns, making it adaptable to various datasets and scenarios.
Demerits
Potential Overfitting to Reconstruction Trajectory
The method's reliance on the reconstruction trajectory may lead to overfitting, particularly if the reconstruction network is not robust enough to capture all relevant patterns.
Potential Computational Intensity
The method's use of reinforcement learning and reconstruction networks may increase computational intensity, particularly for large datasets.
Expert Commentary
The article presents a novel and innovative approach to time series anomaly detection, leveraging a reconstruction-driven boundary negative generation framework to improve anomaly representation learning. While the method shows promising results, further research is needed to address potential limitations, such as overfitting to the reconstruction trajectory. The method's flexibility and adaptability make it an attractive option for various real-world scenarios, and its policy implications are significant. Overall, BoundAD is a valuable contribution to the field of TSAD, and its impact will be increasingly felt as the method is refined and applied to more complex datasets.
Recommendations
- ✓ Further research is needed to address potential limitations, such as overfitting to the reconstruction trajectory and computational intensity.
- ✓ BoundAD should be applied to a wider range of real-world scenarios to demonstrate its practical feasibility and adaptability.
