Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2405.19823v2
• Date: Tue, 20 Aug 2024 08:00:02 GMT
• Title: Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection
• Authors: Junqi Chen, Xu Tan, Sylwan Rahardja, Jiawei Yang, Susanto Rahardja,
• Abstract summary: Deep learning-based sequence models are extensively employed in Time Series Anomaly Detection tasks. 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.
• Score: 25.60381244912307
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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