| --- |
| license: mit |
| pretty_name: PATH |
| tags: |
| - time-series |
| - anomaly-detection |
| - multivariate-time-series |
| - unsupervised-learning |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: normal |
| data_files: |
| - split: data |
| path: 1_postsim/normal.parquet |
| - config_name: anomalous |
| data_files: |
| - split: data |
| path: 1_postsim/anomalous.parquet |
| - config_name: control |
| data_files: |
| - split: data |
| path: 1_postsim/control.parquet |
| - config_name: train |
| data_files: |
| - split: data |
| path: 1_postsim/train.parquet |
| - config_name: test |
| data_files: |
| - split: data |
| path: 1_postsim/test.parquet |
| - config_name: train_clean |
| data_files: |
| - split: data |
| path: 1_postsim/train_clean.parquet |
| - config_name: test_clean |
| data_files: |
| - split: data |
| path: 1_postsim/test_clean.parquet |
| --- |
| |
| # 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](https://doi.org/10.5281/zenodo.13255120), 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](#usage). |
|
|
| - Paper: [doi.org/10.1016/j.bdr.2025.100573](https://doi.org/10.1016/j.bdr.2025.100573) |
| - Source code: [github.com/lcs-crr/PATH](https://github.com/lcs-crr/PATH) |
| - Zenodo: [doi.org/10.5281/zenodo.13255120](https://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: |
|
|
| ```bash |
| 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"]`): |
|
|
| ```bash |
| 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`](https://github.com/lcs-crr/PATH/blob/main/src/1_data.py) in the source code repository. |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite it: |
|
|
| ```bibtex |
| @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 |
|
|
