多任务自监督学习，提升时间序列异常检测的泛化能力
NeuCoReClass AD: Redefining Self-Supervised Time Series Anomaly Detection
- 融合对比、重构与分类任务，构建多代理任务框架
- 在多个基准上优于传统方法和主流深度模型
- 无需领域知识，可自动发现不同类型异常特征
时间序列异常检测在众多实际应用中至关重要。无监督方法中，自监督学习因其无需标注数据即可建模正常行为而受到关注。然而，许多现有方法依赖单一代理任务，难以捕捉正常数据中的深层模式；同时常需针对特定领域设计手工变换，限制了跨任务的泛化能力。为此，我们提出NeuCoReClass AD，一种结合对比、重构与分类代理任务的自监督多任务时间序列异常检测框架。该方法采用神经变换学习生成信息丰富、多样且一致的增强视图，无需领域先验知识。我们在多个基准上评估该方法，结果表明其持续优于经典基线及多数深度学习方法。此外，它能在完全无监督条件下刻画出不同类型的异常模式。
原文摘要 · Abstract (English)
Time series anomaly detection plays a critical role in a wide range of real-world applications. Among unsupervised approaches, self-supervised learning has gained traction for modeling normal behavior without the need of labeled data. However, many existing methods rely on a single proxy task, limiting their ability to capture meaningful patterns in normal data. Moreover, they often depend on handcrafted transformations tailored specific domains, hindering their generalization accross diverse problems. To address these limitations, we introduce NeuCoReClass AD, a self-supervised multi-task time series anomaly detection framework that combines contrastive, reconstruction, and classification proxy tasks. Our method employs neural transformation learning to generate augmented views that are informative, diverse, and coherent, without requiring domain-specific knowledge. We evaluate NeuCoReClass AD across a wide range of benchmarks, demonstrating that it consistently outperforms both classical baselines and most deep-learning alternatives. Furthermore, it enables the characterization of distinct anomaly profiles in a fully unsupervised manner.
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