CATCH-RG: Channel-Aware Multivariate Time-Series Anomaly Detection via Patching with Reliability-Gated Channel Mask Generator

Recognition

This work received an Excellence Award at KAMP 2025 (The 5th K-Artificial Intelligence Manufacturing Data Analysis Competition),
recognizing its robustness-oriented extension of CATCH : Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching (ICLR'25) for real-world anomaly detection scenarios involving imperfect and incomplete multivariate time-series data.

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Overview

CATCH is a channel-aware multivariate time-series anomaly detection method that models cross-channel interactions through frequency-domain patching and channel-aware masking. It is evaluated under the TAB benchmarking protocol, which provides unified and rigorous evaluation for time-series anomaly detection. This repository provides a robust masking extension for CATCH, and proposed reliability-gated channel masking to better handle incomplete and noisy multivariate time-series data, while preserving the original CATCH architecture and interface.

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Motivation

In practical settings, multivariate time-series data often contain:

• missing or partially observed channels,
• noisy or corrupted sensor measurements,
• globally inactive segments due to sensor or communication failures.

The original CATCH framework does not explicitly model channel reliability, which may lead to unstable cross-channel interactions under such conditions.

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Reliability-Gated Mask (RG-Mask)

We extend the channel masking mechanism of CATCH with a Reliability-Gated Mask, which dynamically controls cross-channel information flow based on data-driven channel reliability estimates.

The proposed mask:

• estimates channel reliability using simple, robust statistics,
• softly suppresses interactions involving unreliable channels,
• preserves self-channel connections,
• and collapses to an identity mask when all channels are globally inactive.

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Citation

If you use this work or build upon it, please cite the original CATCH and TAB papers:

@inproceedings{wu2024catch,
  title     = {{CATCH}: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching},
  author    = {Wu, Xingjian and Qiu, Xiangfei and Li, Zhengyu and Wang, Yihang and Hu, Jilin and Guo, Chenjuan and Xiong, Hui and Yang, Bin},
  booktitle = {ICLR},
  year      = {2025}
}