PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series

PATH contains multivariate time series of 16 features, simulated for evaluating online unsupervised anomaly detection. This repository mirrors Zenodo, which remains the archival source. The raw .mat files in 0_simulation are identical to the Zenodo files. The pickle files of 1_postsim are provided here as Parquet with the same names and the same contents. To convert them to the required pickle format after download, see Usage.

• Paper: doi.org/10.1016/j.bdr.2025.100573
• Source code: github.com/lcs-crr/PATH
• Zenodo: doi.org/10.5281/zenodo.13255120

Repository structure

0_simulation/              3,891 raw .mat files
1_postsim/
    normal.parquet         all nominal series
    anomalous.parquet      all anomalous series
    control.parquet        the control counterpart of each anomalous series
    train.parquet          3-fold cross-validation, training sets
    test.parquet           3-fold cross-validation, test sets
    train_clean.parquet    train.parquet without anomalous series
    test_clean.parquet     test.parquet without anomalous series
scripts/                   pkl_to_parquet.py and parquet_to_pkl.py

Usage

0_simulation and 1_postsim can be downloaded separately. The .mat files of 0_simulation are identical to the Zenodo files:

hf download lcs-crr/PATH --type dataset --local-dir PATH --include "0_simulation/*"

The pickle files of 1_postsim are lists of 2D float32 NumPy arrays. The name of the corresponding .mat file, and by extension the label, is in the dtype metadata (array.dtype.metadata["file_name"]):

hf download lcs-crr/PATH --type dataset --local-dir PATH --include "1_postsim/*" --include "scripts/*"
python PATH/scripts/parquet_to_pkl.py --parquet-dir PATH/1_postsim

The script needs numpy and pyarrow.

The 2_preprocessed folder is not included, as its contents are specific to the TensorFlow data pipeline. It is generated from 1_postsim by src/1_data.py in the source code repository.

Citation

If you use this dataset, please cite it:

@misc{correiaPATHDiscretesequenceDataset2025,
  title = {{{PATH}}: {{A Discrete-sequence Dataset}} for {{Evaluating Online Unsupervised Anomaly Detection Approaches}} for {{Multivariate Time Series}}},
  author = {Correia, Lucas and Goos, Jan-Christoph and B{\"a}ck, Thomas and Kononova, Anna V.},
  year = {2025},
  publisher = {Zenodo},
  doi = {10.5281/ZENODO.13255120},
  copyright = {MIT License}
}

License

MIT