CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching

• URL: http://arxiv.org/abs/2410.12261v1
• Date: Wed, 16 Oct 2024 05:58:55 GMT
• Title: CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching
• Authors: Xingjian Wu, Xiangfei Qiu, Zhengyu Li, Yihang Wang, Jilin Hu, Chenjuan Guo, Hui Xiong, Bin Yang,
• Abstract summary: We introduce CATCH, a framework based on frequency patching. We propose a Channel Fusion Module (CFM) which features a patch-wise mask generator and a masked-attention mechanism. Experiments on 9 real-world datasets and 12 synthetic datasets demonstrate that CATCH achieves state-of-the-art performance.
• Score: 24.927390742543707
• License:
• Abstract: Anomaly detection in multivariate time series is challenging as heterogeneous subsequence anomalies may occur. Reconstruction-based methods, which focus on learning nomral patterns in the frequency domain to detect diverse abnormal subsequences, achieve promising resutls, while still falling short on capturing fine-grained frequency characteristics and channel correlations. To contend with the limitations, we introduce CATCH, a framework based on frequency patching. We propose to patchify the frequency domain into frequency bands, which enhances its ability to capture fine-grained frequency characteristics. To perceive appropriate channel correlations, we propose a Channel Fusion Module (CFM), which features a patch-wise mask generator and a masked-attention mechanism. Driven by a bi-level multi-objective optimization algorithm, the CFM is encouraged to iteratively discover appropriate patch-wise channel correlations, and to cluster relevant channels while isolating adverse effects from irrelevant channels. Extensive experiments on 9 real-world datasets and 12 synthetic datasets demonstrate that CATCH achieves state-of-the-art performance.

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