用深度波浪概率网络检测非平稳时间序列异常，无需假设分布。
Anomaly Detection for Non-stationary Time Series using Recurrent Wavelet Probabilistic Neural Network
- 用递归编码器解码器捕捉时序特征，波浪概率网络构建概率模型。
- 在45个真实数据集上表现优于现有方法，可提前预警异常事件。
- 不依赖分布假设，适应不同数据变化速率，适合工业监控等场景。
本文提出一种无监督的循环波浪概率神经网络（RWPNN），用于在非平稳环境下检测时间序列异常。该框架由两个模块构成：堆叠式循环编码器-解码器（SREnc-Dec）用于在潜在空间中捕捉时序特征；多感受野波浪概率网络（MRWPN）则构建集成概率模型以表征潜在空间。该方法将标准波浪概率网络扩展为波浪深度概率网络，可处理更高维度数据。MRWPN模块无需强分布假设即可适应不同数据变化速率，提升了非平稳环境下的时间序列异常检测（TSAD）鲁棒性与准确性。我们在45个来自不同领域的实际时间序列数据集上评估了RWPNN性能，验证其在多种约束条件下的有效性，并展示了对异常事件提供早期预警的能力。
原文摘要 · Abstract (English)
In this paper, an unsupervised Recurrent Wavelet Probabilistic Neural Network (RWPNN) is proposed, which aims at detecting anomalies in non-stationary environments by modelling the temporal features using a nonparametric density estimation network. The novel framework consists of two components, a Stacked Recurrent Encoder-Decoder (SREnc-Dec) module that captures temporal features in a latent space, and a Multi-Receptive-field Wavelet Probabilistic Network (MRWPN) that creates an ensemble probabilistic model to characterise the latent space. This formulation extends the standard wavelet probabilistic networks to wavelet deep probabilistic networks, which can handle higher data dimensionality. The MRWPN module can adapt to different rates of data variation in different datasets without imposing strong distribution assumptions, resulting in a more robust and accurate detection for Time Series Anomaly Detection (TSAD) tasks in the non-stationary environment. We carry out the assessment on 45 real-world time series datasets from various domains, verify the performance of RWPNN in TSAD tasks with several constraints, and show its ability to provide early warnings for anomalous events.
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