Deep learning-based sequence models are extensively employed in Time Series Anomaly Detection (TSAD) tasks due to their effective sequential modeling capabilities. However, the ability of TSAD is limited by two key challenges: (i) the ability to model long-range dependency and (ii) the generalization issue in the presence of non-stationary data. To tackle these challenges, an anomaly detector that leverages the selective state space model known for its proficiency in capturing long-term dependencies across various domains is proposed. Additionally, a multi-stage detrending mechanism is introduced to mitigate the prominent trend component in non-stationary data to address the generalization issue. Extensive experiments conducted on realworld public datasets demonstrate that the proposed methods surpass all 12 compared baseline methods.

翻译：基于深度学习的序列模型因其有效的序列建模能力，在时间序列异常检测任务中得到了广泛应用。然而，TSAD 的能力受到两个关键挑战的限制：(i) 对长程依赖关系的建模能力，以及 (ii) 在非平稳数据存在时的泛化问题。为应对这些挑战，本文提出了一种利用选择性状态空间模型的异常检测器，该模型以其在多个领域中捕捉长期依赖关系的能力而著称。此外，引入了一种多阶段去趋势机制，以抑制非平稳数据中显著的趋势成分，从而解决泛化问题。在真实世界公共数据集上进行的大量实验表明，所提出的方法超越了所有 12 种对比基线方法。