CATCH: 基于频率分块的信道感知多变量时间序列异常检测
CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching
AI总结：
提出CATCH框架，通过频率分块增强细粒度频率特征捕获，并设计通道融合模块（CFM）利用分块掩码和掩码注意力机制，结合双层多目标优化学习信道相关性，在10个真实和12个合成数据集上达到最先进性能。
摘要：
多变量时间序列中的异常检测具有挑战性，因为可能发生异构子序列异常。基于重构的方法专注于学习频域中的正常模式以检测多样的异常子序列，取得了有希望的结果，但在捕获细粒度频率特征和通道相关性方面仍存在不足。为了解决这些局限性，我们引入了CATCH，一个基于频率分块的框架。我们提出将频域分块为频带，增强了其捕获细粒度频率特征的能力。为了感知适当的通道相关性，我们提出了通道融合模块（CFM），该模块具有分块掩码生成器和掩码注意力机制。在双层多目标优化算法的驱动下，CFM被鼓励迭代地发现适当的分块级通道相关性，并在隔离不相关通道的负面影响的同时聚类相关通道。在10个真实世界数据集和12个合成数据集上的大量实验表明，CATCH达到了最先进的性能。我们在以下网址提供代码和数据集：此 https URL。
英文摘要：
Anomaly detection in multivariate time series is challenging as heterogeneous subsequence anomalies may occur. Reconstruction-based methods, which focus on learning normal patterns in the frequency domain to detect diverse abnormal subsequences, achieve promising results, 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 10 real-world datasets and 12 synthetic datasets demonstrate that CATCH achieves state-of-the-art performance. We make our code and datasets available at https://github.com/decisionintelligence/CATCH.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
