Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study

ArXi:2604.13928v1 Announce Type: new Industrial time-series data from real production environments exhibits substantially higher complexity than commonly used benchmark datasets, primarily due to heterogeneous, multi-stage operational processes. As a result, anomaly detection methods validated under simplified conditions often fail to generalize to industrial settings. This work presents an empirical study on a unique dataset collected from fully operational industrial machinery, explicitly capturing pronounced process-induced variability.