DACR：用于时间序列异常检测的分布增强对比重建
DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection
AI总结：
针对复杂动态场景下正常数据多分布、异常差异程度不一的问题，提出DACR框架，通过分布外数据生成、对比学习和注意力依赖建模提升重建鲁棒性，并在九个基准上取得最先进性能。
摘要：
时间序列数据中的异常检测对于在众多应用中识别故障、失效、威胁和离群点至关重要。近年来，深度学习技术已被应用于这一主题，但在复杂且高度动态的真实场景中往往表现不佳，例如正常数据可能由多种分布组成，且不同类型的异常与正常数据之间的差异程度也各不相同。在本工作中，为应对这些挑战，我们提出了分布增强对比重建（Distribution-Augmented Contrastive Reconstruction，DACR）。DACR生成与正常数据分布不相交的额外数据，以压缩正常数据的表示空间，并通过对比学习增强特征提取器，从而更好地捕捉时间序列数据中的内在语义。此外，DACR采用注意力机制对多变量时间序列特征之间的语义依赖关系进行建模，从而实现更鲁棒的异常检测重建。在涵盖多种异常检测场景的九个基准数据集上开展的大量实验表明，DACR能够有效达到时间序列异常检测的全新最先进水平。
英文摘要：
Anomaly detection in time-series data is crucial for identifying faults, failures, threats, and outliers across a range of applications. Recently, deep learning techniques have been applied to this topic, but they often struggle in real-world scenarios that are complex and highly dynamic, e.g., the normal data may consist of multiple distributions, and various types of anomalies may differ from the normal data to different degrees. In this work, to tackle these challenges, we propose Distribution-Augmented Contrastive Reconstruction (DACR). DACR generates extra data disjoint from the normal data distribution to compress the normal data's representation space, and enhances the feature extractor through contrastive learning to better capture the intrinsic semantics from time-series data. Furthermore, DACR employs an attention mechanism to model the semantic dependencies among multivariate time-series features, thereby achieving more robust reconstruction for anomaly detection. Extensive experiments conducted on nine benchmark datasets in various anomaly detection scenarios demonstrate the effectiveness of DACR in achieving new state-of-the-art time-series anomaly detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
