LPCVAE通过融合时频信息与长时依赖，提升时间序列异常检测精度。
LPCVAE: A Conditional VAE with Long-Term Dependency and Probabilistic Time-Frequency Fusion for Time Series Anomaly Detection
- 用LSTM捕捉窗口外的长期时序依赖，突破单窗口限制。
- 采用PoE机制实现时频特征的分布级自适应融合，减少信息损失。
- 在4个公开数据集上优于当前最优方法，适合复杂系统监控场景。
时间序列异常检测（TSAD）是信号处理中的关键任务，保障复杂系统的可靠性。基于重构的方法在TSAD中占据主导地位，其中基于变分自编码器（VAE）的方法已取得显著成效。然而，现有方法受限于单窗口特征表示，且未能充分挖掘长期时序与频域信息。为此，本文提出条件变分自编码器LPCVAE，引入长短期记忆网络（LSTM）以捕捉窗口外的长期依赖；同时结合乘积专家（Product-of-Experts, PoE）机制，实现时频特征在分布层面的自适应概率融合，有效缓解时频信息丢失问题。在四个公开数据集上的大量实验表明，该方法优于当前最先进方法。结果验证了融合长期时序与频域表征并结合自适应融合策略，能构建鲁棒高效的TSAD解决方案。
原文摘要 · Abstract (English)
Time series anomaly detection(TSAD) is a critical task in signal processing field, ensuring the reliability of complex systems. Reconstruction-based methods dominate in TSAD. Among these methods, VAE-based methods have achieved promising results. Existing VAE-based methods suffer from the limitation of single-window feature and insufficient leveraging of long-term time and frequency information. We propose a Conditional Variational AutoEncoder with Long-term dependency and Probabilistic time-frequency fusion, named LPCVAE. LPCVAE introduces LSTM to capture long-term dependencies beyond windows. It further incorporates a Product-of-Experts (PoE) mechanism for adaptive and distribution-level probabilistic fusion. This design effectively mitigates time-frequency information loss. Extensive experiments on four public datasets demonstrate it outperforms state-of-the-art methods. The results confirm that integrating long-term time and frequency representations with adaptive fusion yields a robust and efficient solution for TSAD.
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