Stable Implicit Conditioning With Residual Statistics for Multivariate Time-Series Anomaly Detection in Industrial IoT Monitoring
Abstract
Timely anomaly detection in Industrial Internet of Things (IIoT) monitoring requires robust modeling of noisy multivariate sensor streams. Although decomposition-derived residuals provide a potentially useful auxiliary view for multivariate time-series anomaly detection (MTSAD), they are not clean anomaly surrogates in unsupervised settings, as they also contain normal fluctuations, sensor noise, and decomposition artifacts. Therefore, the key challenge is not simply whether residual evidence is useful, but how it can be integrated stably without disturbing the backbone’s native anomaly discrimination process. To address this issue, we propose stable implicit conditioning (SIC), a lightweight and general framework for decomposition-aware unsupervised MTSAD. Instead of reusing residual evidence through explicit downstream intervention, SIC converts compact residual statistics into a bounded sample-dependent channelwise calibration signal for mild representation-level adaptation. When instantiated on the anomaly Transformer (AT), the resulting AT-SIC improves upon the reproduced backbone on four out of five public benchmarks. Additional analyses on decomposition choices, structured disturbances, boundary cases, and cross-backbone transfer show that SIC provides a more reliable evidence utilization path than several intuitive explicit reuse strategies, while introducing only negligible efficiency overhead. These results suggest that decomposition-derived residual evidence is better exploited implicitly than explicitly in this setting.