通过条件归一化流的归纳偏差进行时间序列中的异常检测
Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows
AI总结：
本文通过在条件归一化流中引入显式归纳偏差，将异常检测从观测空间转移到潜在空间，以更准确地捕捉时间序列的结构动态，从而提高异常检测的鲁棒性和可解释性。
摘要：
深度生成模型用于多变量时间序列的异常检测通常是通过最大化数据似然来训练的。然而，观测空间中的似然度测量的是边缘密度而不是对结构化时间动态的符合性，因此可能给异常或分布外的样本分配高概率。我们通过将异常的概念转移到预设的潜在空间中来解决这一结构限制。我们引入了条件归一化流中的显式归纳偏差，以离散时间状态空间框架建模时间序列观测，将潜在表示限制为按照预设的时间动态演变。在此框架下，预期行为对应于符合指定的潜在轨迹分布，而异常则定义为对这些动态的违反。因此，异常检测被减少为一个基于统计的符合性测试，即样本被映射到潜在空间并通过与预设潜在演变的拟合度测试进行评估。这产生了一个原理性的决策规则，即使在高观测似然区域也能保持有效。在合成和真实世界时间序列上的实验显示，能够可靠地检测频率、幅度和观测噪声中的异常，同时提供可解释的模型符合性诊断。
英文摘要：
Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing observed data likelihood. However, likelihood in observation space measures marginal density rather than conformity to structured temporal dynamics, and therefore can assign high probability to anomalous or out-of-distribution samples. We address this structural limitation by relocating the notion of anomaly to a prescribed latent space. We introduce explicit inductive biases in conditional normalizing flows, modeling time-series observations within a discrete-time state-space framework that constrains latent representations to evolve according to prescribed temporal dynamics. Under this formulation, expected behavior corresponds to compliance with a specified distribution over latent trajectories, while anomalies are defined as violations of these dynamics. Anomaly detection is consequently reformulated as a statistically grounded compliance test, such that observations are mapped to latent space and evaluated via goodness-of-fit tests against the prescribed latent evolution. This yields a principled decision rule that remains effective even in regions of high observation likelihood. Experiments on synthetic and real-world time-series demonstrate reliable detection of anomalies in frequency, amplitude, and observation noise, while providing interpretable diagnostics of model compliance.
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