Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 46 references that are in the map. First posted on arXiv 17 Jun 2026.
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25 of 46 references.
References · 25
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13 more
- 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 131
- 2022Deep Isolation Forest for Anomaly Detectioncited by 92
- 2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
- 2020Challenges in Benchmarking Stream Learning Algorithms with Real-world Datacited by 59
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Citing papers per year: 2026: 1.
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