A Dataset for Evaluating Online Anomaly Detection Approaches for Discrete Multivariate Time Series

The data hosted here belongs inside the 1_postsim folder, which consists of the following pickle files:

• normal.pkl, which contains all nominal sequences
• anomalous.pkl, which contains all anomalous sequences
• control.pkl, which contains all control-counterparts to anomalous.pkl
• training.pkl, which contains all pre-determined folds for training
• training_clean.pkl, a version of training.pkl without anomalous sequences
• testing.pkl, which contains all pre-determined folds for testing
• testing_clean.pkl, a version of testing.pkl without anomalous sequences

Each pickle file is a list of several 2D NumPy arrays, each representing a multivariate time series. The name of the corresponding .mat file (and, by extension, the label) is present in the metadata. For NumPy object array, it can be read by calling array.dtype.metadata['file_name'].

The raw simulation output sequences in the 0_simulation folder are not provided due to the Zenodo file number limit of 100 files. We decided to omit the data belonging to the 2_preprocessed folder as the contents are specific to the TensorFlow data pipeline and the same data host limitations would apply.

For more information, refer to the publication (preprint): https://arxiv.org/abs/2411.13951

For access to the source code, refer to the repository on GitHub: https://github.com/lcs-crr/PATH

If you use this dataset for your research, please consider citing it through the menu on the right, or using the following bibtex entry:
@misc{correiaDatasetEvaluatingOnline2024a,
  title = {A {{Dataset}} for {{Evaluating Online Anomaly Detection Approaches}} for {{Discrete Multivariate Time Series}}},
  author = {Correia, Lucas and Goos, Jan-Christoph and B{\"a}ck, Thomas and Kononova, Anna V.},
  year = {2024},
  publisher = {Zenodo},
  doi = {10.5281/ZENODO.13255121},
  copyright = {MIT License}
}