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<p>Unsupervised anomaly detection in multivariate time series is critical for the reliability of the Industrial Internet of Things (IIoT) and IT operations. However, existing reconstruction-based approaches struggle with anomaly contamination, complex dependency modeling, and non-stationary interference. To address these challenges, this paper proposes TFD-CAD, a novel Collaborative Time-Frequency Dual-Branch Anomaly Detection framework. Distinct from methods that process domains in isolation, TFD-CAD introduces a collaborative masking mechanism where temporal features explicitly guide high-frequency masking to purify representations against contamination. To capture intricate dependencies, a multi-scale fusion module is designed within the frequency branch to model intra-band details and inter-band harmonic correlations simultaneously. Furthermore, non-stationarity is addressed via a residual-focused reconstruction strategy. By leveraging time-series decomposition and static memory-guided attention entropy regularization, the model effectively disentangles legitimate macro-trends from anomalies. Extensive experiments on multiple real-world benchmark datasets demonstrate that TFD-CAD achieves state-of-the-art performance, significantly outperforming existing baselines in terms of detection accuracy and robustness.</p>

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TFD-CAD: Collaborative time-frequency dual-branch framework for time series anomaly detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月13日

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  • Jintao Chen,
  • Peishun Liu,
  • Shuangyin Li,
  • Peng Li,
  • Xinyue Li,
  • Ruixin Hua

摘要

Unsupervised anomaly detection in multivariate time series is critical for the reliability of the Industrial Internet of Things (IIoT) and IT operations. However, existing reconstruction-based approaches struggle with anomaly contamination, complex dependency modeling, and non-stationary interference. To address these challenges, this paper proposes TFD-CAD, a novel Collaborative Time-Frequency Dual-Branch Anomaly Detection framework. Distinct from methods that process domains in isolation, TFD-CAD introduces a collaborative masking mechanism where temporal features explicitly guide high-frequency masking to purify representations against contamination. To capture intricate dependencies, a multi-scale fusion module is designed within the frequency branch to model intra-band details and inter-band harmonic correlations simultaneously. Furthermore, non-stationarity is addressed via a residual-focused reconstruction strategy. By leveraging time-series decomposition and static memory-guided attention entropy regularization, the model effectively disentangles legitimate macro-trends from anomalies. Extensive experiments on multiple real-world benchmark datasets demonstrate that TFD-CAD achieves state-of-the-art performance, significantly outperforming existing baselines in terms of detection accuracy and robustness.