  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.
