Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs

• URL: http://arxiv.org/abs/2606.18898v1
• Date: Wed, 17 Jun 2026 10:17:16 GMT
• Title: Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs
• Abstract summary: Real-world data is often sparse, irregularly sampled or partially observed, yet existing methods assume uniformly sampled time series.<n>We propose a generative approach based on Latent SDEs that projects the observed time series on a continuous-time dynamical system.<n> Experiments on six anomaly benchmark datasets show that our proposed method ranks first among state-of-the-art baselines.
• Score: 6.371871362888748
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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