Outlier/Anomaly Detection of Univariate Time Series: A Dataset Collection and Benchmark

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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/.

Original languageEnglish
Title of host publicationBig Data Analytics and Knowledge Discovery - 24th International Conference, DaWaK 2022, Proceedings
EditorsRobert Wrembel, Johann Gamper, Gabriele Kotsis, Ismail Khalil, A Min Tjoa
PublisherSpringer
Pages163-169
Number of pages7
ISBN (Print)9783031126697
DOIs
Publication statusPublished - 2022
Event24th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2022 - Vienna, Austria
Duration: 22 Aug 202224 Aug 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13428 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2022
Country/TerritoryAustria
CityVienna
Period22.08.202224.08.2022

Keywords

  • Anomaly detection
  • Outlier detection
  • Time series

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