@inproceedings{bcdb3813a8c74ff2b28de361b77f2dff,
title = "Outlier/Anomaly Detection of Univariate Time Series: A Dataset Collection and Benchmark",
abstract = "In this paper, we present an extensive collection of outlier/anomaly detection tasks to identify unusual series from a given time series dataset. The presented work is based on the popular UCR time series classification archive. In addition to the detection tasks, we provide curated benchmarks, an evaluation scheme and baseline results. The resulting unusual time series detection collection is openly available at: https://outlier-detection.github.io/utsd/.",
keywords = "Anomaly detection, Outlier detection, Time series",
author = "David Muhr and Michael Affenzeller",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 24th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2022 ; Conference date: 22-08-2022 Through 24-08-2022",
year = "2022",
doi = "10.1007/978-3-031-12670-3_14",
language = "English",
isbn = "9783031126697",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "163--169",
editor = "Robert Wrembel and Johann Gamper and Gabriele Kotsis and Ismail Khalil and Tjoa, {A Min}",
booktitle = "Big Data Analytics and Knowledge Discovery - 24th International Conference, DaWaK 2022, Proceedings",
address = "Germany",
}