用随机微分方程建模稀疏不规则多变量时间序列，提升异常检测鲁棒性。
Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs
- 基于隐变量随机微分方程，直接处理缺失值与不规则采样。
- 在6个基准数据集上优于现有方法，稀疏场景下性能更稳定。
- 适合工业监控、医疗等存在采样不规律的真实场景。
多变量时间序列异常检测（MTSAD）在工业监测、网络安全和医疗等领域至关重要。真实数据常呈稀疏、非均匀采样或部分观测状态，而现有方法多假设数据均匀采样。本文提出一种基于隐变量随机微分方程（Latent SDEs）的生成方法，将观测时间序列投影到连续时间随机动力系统中，可直接处理缺失观测与不规则采样，并自然捕捉许多实际场景中存在的周期性行为。在六个异常检测基准数据集上的实验表明，所提方法在主流基线中排名第一。进一步验证显示，在严重数据稀疏条件下，本方法仍保持稳健，而对比基线性能显著下降。结果表明，隐变量SDEs是多变量时间序列异常检测中应对现实不规则性的天然归纳偏置。
原文摘要 · Abstract (English)
Multivariate time series anomaly detection (MTSAD) is critical for a wide range of application areas, such as industrial monitoring, cybersecurity, or healthcare. Real-world data is often sparse, irregularly sampled or partially observed, yet existing methods assume uniformly sampled time series. We propose a generative approach based on Latent SDEs that projects the observed time series on a continuous-time stochastic dynamical system, directly being able to handle missing observations and irregular sampling, while also naturally capturing possible cyclic behavior that many real-world use cases inherently possess. Experiments on six anomaly benchmark datasets show that our proposed method ranks first among state-of-the-art baselines. We further demonstrate that our method remains robust under severe data sparsity, while performance significantly degrades for the tested baseline methods. These results highlight latent SDEs as a natural inductive bias for anomaly detection in multivariate time series, especially in presence of real-world irregularities.
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