nirojasva/spectral-benchmark

Benchmark for Anomaly Detection on Spectral Data Streams

This dataset contains the spectral data used to evaluate comparative methods, including the Online Bootstrapping K-Nearest Neighbor (OBKNN) algorithm, for online anomaly detection.

Data Structure

The data files are organized in a tabular format with the following dimensional characteristics:

• The first column indicates the timestamp.
• The intermediate columns represent the captured spectral wavelengths.
• The last column contains the binary anomaly label, where 1 indicates an anomaly and 0 indicates a normal state.

Academic Citation

You can cite the original work using the following BibTeX format:

@inproceedings{varela2026onlinebootknn,
  title={OnlineBootKNN: An Unsupervised Framework for Detecting Anomalies in Spectral Data Streams},
  author={Rojas Varela, Nicolas and Ah-Pine, Julien and Mephu Nguifo, Engelbert},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={40},
  number={18},
  pages={15797--15805},
  year={2026}
}