基于潜在随机微分方程的稀疏不规则多元时间序列异常检测
Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs
AI总结：
针对现实世界中稀疏、不规则采样的多元时间序列，提出基于潜在随机微分方程的生成方法，将观测投影到连续时间随机动力系统，处理缺失和不规则采样，并捕获循环行为，在六个基准数据集上取得最优结果。
摘要：
多元时间序列异常检测（MTSAD）在工业监控、网络安全或医疗保健等广泛应用领域至关重要。现实世界的数据通常是稀疏的、不规则采样的或部分观测的，但现有方法假设时间序列均匀采样。我们提出了一种基于潜在随机微分方程的生成方法，将观测到的时间序列投影到一个连续时间随机动力系统上，能够直接处理缺失观测和不规则采样，同时自然捕获许多现实世界用例固有的可能循环行为。在六个异常基准数据集上的实验表明，我们提出的方法在现有最先进基线中排名第一。我们进一步证明，在严重数据稀疏性下，我们的方法保持鲁棒性，而测试的基线方法性能显著下降。这些结果突显了潜在随机微分方程作为多元时间序列异常检测的自然归纳偏置，尤其是在存在现实世界不规则性的情况下。
英文摘要：
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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