The BV4 Benchmark for Unsupervised Anomaly Detection in High-Dimensional Spectral Data Streams

This paper introduces the BV4 Benchmark, a collection of high-dimensional spectral datasets for unsupervised anomaly detection in vacuum environment data streams. Collected via Optical Emission Spectroscopy (OES), the benchmark comprises nine experiments representing practical scenarios structured according to established anomaly detection literature and validated by domain experts to address challenges such as spatial anomalies, temporal anomalies, and concept drift. BV4 datasets are composed of 2048 distinct wavelengths recorded over time, accompanied by timestamps and ground truth labels, enabling rigorous evaluation of machine learning models under dynamic environmental changes within vacuum chambers. We demonstrate the utility of BV4 through a simple comparative evaluation against previous datasets for anomaly detection in spectral data streams and discuss the current limitations and future improvements of the benchmark to support meaningful evaluative claims in the area.