Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs
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Computer Science > Machine Learning
Title:Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs
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.
| Comments: | Preprint |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.18898 [cs.LG] |
| (or arXiv:2606.18898v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18898
arXiv-issued DOI via DataCite (pending registration)
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