Abstract
Existing self-supervised multivariate time series anomaly detection methods struggle with interference among variables during reconstruction. They also tend to miss capturing critical anomaly information, resulting in unsatisfactory performance, especially in scenarios with strong mechanistic contexts. To this end, we propose a targeted anomaly detection algorithm called inference stacked recurrent autoencoder (ISRAE). Its key contribution lies in the design of a specific inference kernel, derived from specialist knowledge, which captures the strong mechanistic relationships among variables. This kernel is then fused with the multidimensional anomalies predicted by the SRAE, which mitigates interference among variables through the stacking technique. Furthermore, a novel differential constraint is introduced into the loss function, which not only highlights anomaly reconstruction errors, but also smooths the reconstructions, enhancing overall detection performance. Comprehensive comparison experiments and ablation studies show that ISRAE achieves superior anomaly detection performance under strong mechanistic contexts and highlight the importance of each key module in ISRAE.
Original language | English |
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Pages (from-to) | 1-10 |
Number of pages | 10 |
Journal | IEEE Transactions on Industrial Informatics |
Early online date | 13 Nov 2024 |
DOIs | |
Publication status | E-pub ahead of print - 13 Nov 2024 |
Keywords
- Anomaly detection
- inference kernel
- inference stacked recurrent-autoencoder (ISRAE)
- multivariate time series
- self-supervised method
- strong mechanistic context
- inference stacked recurrent-Autoencoder (ISRAE)