Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs
For practitioners in industrial monitoring, healthcare, etc., this provides a robust anomaly detection method for real-world irregular time series, where existing methods fail.
The paper tackles anomaly detection in sparse and irregular multivariate time series, proposing a generative method based on Latent SDEs that handles missing data and irregular sampling. It achieves state-of-the-art results on six benchmarks and remains robust under severe sparsity, unlike baselines.
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.