{"data":{"id":"10.5281/zenodo.13255120","type":"dois","attributes":{"doi":"10.5281/zenodo.13255120","prefix":"10.5281","suffix":"zenodo.13255120","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Correia, Lucas","nameType":"Personal","givenName":"Lucas","familyName":"Correia","affiliation":["Leiden University","Mercedes-Benz (Germany)"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-3653-5934","nameIdentifierScheme":"ORCID"}]},{"name":"Goos, Jan-Christoph","nameType":"Personal","givenName":"Jan-Christoph","familyName":"Goos","affiliation":["Mercedes-Benz (Germany)"],"nameIdentifiers":[{"nameIdentifier":"0009-0002-7063-3615","nameIdentifierScheme":"ORCID"}]},{"name":"Bäck, Thomas","nameType":"Personal","givenName":"Thomas","familyName":"Bäck","affiliation":["Leiden University"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-6768-1478","nameIdentifierScheme":"ORCID"}]},{"name":"Kononova, Anna V.","nameType":"Personal","givenName":"Anna V.","familyName":"Kononova","affiliation":["Leiden University"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-4138-7024","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series"}],"publisher":"Zenodo","container":{},"publicationYear":2025,"subjects":[],"contributors":[],"dates":[{"date":"2025","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsOriginalFormOf","relatedIdentifier":"https://huggingface.co/datasets/lcs-crr/PATH","resourceTypeGeneral":"Dataset","relatedIdentifierType":"URL"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.13255121","relatedIdentifierType":"DOI"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.14892756","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1","rightsList":[{"rights":"MIT License","rightsUri":"https://opensource.org/licenses/MIT","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"mit","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This dataset is also available on Hugging Face, which offers faster downloads. The 0_simulation folder is identical. The 1_postsim pickle files are provided as Parquet files, together with a script that converts them back to the pickle files described below.\n\n\nThis record comprises the 0_simulation and 1_postsim folders. The 0_simulation folder consists of the raw .mat files, each representing a multivariate time series, whereas the 1_postsim folder consists of the following pickle files:\n\n\n\nnormal.pkl, which contains all nominal sequences\n\nanomalous.pkl, which contains all anomalous sequences\n\ncontrol.pkl, which contains all control-counterparts to anomalous.pkl\n\ntrain.pkl, which contains all pre-determined folds for training\n\ntrain_clean.pkl, a version of train.pkl without anomalous sequences\n\ntest.pkl, which contains all pre-determined folds for testing\n\ntest_clean.pkl, a version of test.pkl without anomalous sequences\n\n\nEach pickle file is a list of 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'].\n\nWe decided to omit the 2_preprocessed folder as the contents are specific to the TensorFlow data pipeline and the same data host limitations would apply.\n\nFor more information, refer to the publication.\n\nFor access to the source code, refer to the repository on GitHub.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{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 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