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

2024-10-16

arXiv.org

AI Summary

Existing reconstruction-based methods for heterogeneous subsequence anomaly detection in multivariate time series struggle to simultaneously achieve fine-grained frequency modeling and dynamic inter-channel dependency learning. Method: We propose a frequency-domain patching reconstruction framework. It introduces *frequency patching*—a novel mechanism that partitions the frequency spectrum into patches for band-level fine-grained feature representation—and a *Channel Fusion Module (CFM)* that employs patch-wise masking and masked attention to adaptively learn and cluster inter-channel dependencies. The framework integrates frequency-domain transformation, patch-based encoding, masked attention, and a two-level multi-objective optimization. Results: Evaluated on 10 real-world and 12 synthetic datasets, our method achieves state-of-the-art performance, significantly improving detection rates for subtle, localized, and cross-channel anomalies.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• Detect anomalies in multivariate time series
• Enhance fine-grained frequency characteristics capture
• Improve channel correlation perception in anomaly detection

Innovation

• Frequency patching enhances anomaly detection
• Channel Fusion Module optimizes correlations
• Bi-level multi-objective algorithm drives performance