Revisiting OmniAnomaly for Anomaly Detection: performance metrics and comparison with PCA-based models
Deep learning models have become the dominant approach for multivariate time series anomaly detection (MTSAD), often reporting substantial performance improvements over classical statistical methods. However, these gains are frequently evaluated under heterogeneous thresholding strategies and evaluation protocols, making fair comparisons difficult. This work revisits OmniAnomaly, a widely used stochastic recurrent model for MTSAD, and systematically compares it with a simple linear baseline based on Principal Component Analysis (PCA) on the Server Machine Dataset (SMD). Both methods are evaluated under identical thresholding and evaluation procedures, with experiments repeated across 100 ...
| Authors | Bruna Alves, Ana Martins, Armando J. Pinho et al. |
|---|---|
| Primary category | stat.ML |
| Published | 19 March 2026 |
| Last updated at source | 19 June 2026 |
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