An online anomalous time series detection algorithm for univariate data streams

Huaming Huang, Kishan Mehrotra, Chilukuri K. Mohan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We address the online anomalous time series detection problem among a set of series, combining three simple distance measures. This approach, akin to control charts, makes it easy to determine when a series begins to differ from other series. Empirical evidence shows that this novel online anomalous time series detection algorithm performs very well, while being efficient in terms of time complexity, when compared to approaches previously discussed in the literature.

Original languageEnglish (US)
Title of host publicationRecent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings
Pages151-160
Number of pages10
DOIs
StatePublished - Aug 16 2013
Event26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013 - Amsterdam, Netherlands
Duration: Jun 17 2013Jun 21 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7906 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013
CountryNetherlands
CityAmsterdam
Period6/17/136/21/13

Keywords

  • Outlier detection
  • anomalous time series detection
  • online algorithms
  • real time detection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Huang, H., Mehrotra, K., & Mohan, C. K. (2013). An online anomalous time series detection algorithm for univariate data streams. In Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings (pp. 151-160). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7906 LNAI). https://doi.org/10.1007/978-3-642-38577-3_16