贝叶斯张量自编码器与物理信息预测先验用于多维时间序列异常检测
Bayesian Tensor Autoencoder with Physics-informed Predictive Prior for Multi-dimensional Time Series Anomaly Detection
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中文总结 AI 辅助
针对多维时间序列异常检测中重塑操作破坏内在相关性的问题，提出贝叶斯融合预测先验的张量自编码器框架PPPTAE，融入物理低秩分解规则，在真实数据集上验证了有效性。
中文摘要 AI 辅助
多维时间序列本质上具有张量结构，在实践中十分常见。尽管时间序列异常检测已取得巨大进展，但大多数现有方法仅限于单变量或多变量时间序列。使用这些方法处理多维时间序列时，需要进行重塑操作，这不可避免地破坏了内在相关性，从而导致性能下降。在单变量/多变量时间序列异常检测中，自编码器（AE）被广泛采用，通常分为基于重建和基于预测两类。基于重建的自编码器利用当前观测进行重建，而基于预测的自编码器利用历史信息预测当前观测。因此，两种自编码器利用的信息不同。为了弥合基于重建和基于预测的自编码器之间的差距，从而充分利用可用信息并进一步提升性能，我们提出了一种预测先验，并将其纳入基于重建的自编码器中。构思这一想法或许并不困难，但设计能够适用于张量异常检测的预测先验却并非易事。具体而言，为避免破坏多维时间序列的内在相关性，我们采用张量自编码器作为主干网络。为将预测先验纳入基于重建的自编码器，我们提出了一种贝叶斯融合方法，分析表明该方法能增强模型对正常数据的建模能力。为缓解自编码器的过泛化问题，我们将物理定律（即张量低秩分解规则）融入预测先验中的神经网络，由此构建了物理信息预测先验张量自编码器（PPPTAE）框架。在真实数据集上的实验结果表明了所提方法的有效性。
英文摘要
Multi-dimensional time series, inherently tensorial, are common in practice. Despite great progress in time series anomaly detection, most existing methods are confined to uni-/multi-variate time series. When handling multi-dimensional time series using these methods, reshaping operations are required, which inevitably break the intrinsic correlations and thus lead to performance degradation. In uni-/multi-variate time series anomaly detection, AutoEncoders (AEs) are widely adopted and generally categorized into reconstruction-based and prediction-based AEs. The reconstruction-based AE utilizes the current observation for reconstruction, while the prediction-based AE utilizes the historical information to predict the current observation. Thus, the two AEs utilize different information. To bridge the gap between reconstruction-based and prediction-based AEs, so as to fully leverage the available information and thus further enhance performance, we propose a predictive prior and incorporate it into the reconstruction-based AE. It may not be very difficult to conceive this idea, but designing the predictive prior so that it can work for tensor anomaly detection is non-trivial. Specifically, to avoid breaking the intrinsic correlations within the multi-dimensional time series, we use the tensor AE as the backbone. To incorporate the predictive prior into the reconstruction-based AE, we propose a Bayesian fusion approach and our analysis reveals that this approach can enhance the modeling capability of the model for normal data. To mitigate the over-generalization problem of AE, we incorporate physical laws, i.e. tensor low-rank decomposition rules, into the neural networks in the predictive prior, leading to the Physics-informed Predictive Prior Tensor AE (PPPTAE) framework. Experimental results on real-world datasets demonstrate the effectiveness of the proposed method.
发表机构
- Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。
