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Matrix profile XIII: Time series snippets: A new primitive for time series data mining

  • Shima Imani
  • , Frank Madrid
  • , Wei Ding
  • , Scott Crouter
  • , Eamonn Keogh
  • University of California
  • University of Tennessee, Knoxville

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Perhaps the most basic query made by a data analyst confronting a new data source is “Show me some representative/typical data.” Answering this question is trivial in many domains, but surprisingly, it is very difficult in large time series datasets. The major difficulty is not time or space complexity, but defining what it means to be representative data in this domain. In this work, we show that the obvious candidate definitions: motifs, shapelets, cluster centers, random samples etc., are all poor choices. Thus motivated, we introduce time series snippets, a novel representation of typical time series subsequences. Beyond their utility for visualizing and summarizing massive time series collections, we show that time series snippets have utility for high-level comparison of large time series collections.

Original languageEnglish
Title of host publicationProceedings - 9th IEEE International Conference on Big Knowledge, ICBK 2018
EditorsOng Yew Soon, Huanhuan Chen, Xindong Wu, Charu Aggarwal
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages382-389
Number of pages8
ISBN (Electronic)9781538691243
DOIs
StatePublished - Dec 24 2018
Event9th IEEE International Conference on Big Knowledge, ICBK 2018 - Singapore, Singapore
Duration: Nov 17 2018 → Nov 18 2018

Publication series

NameProceedings - 9th IEEE International Conference on Big Knowledge, ICBK 2018

Conference

Conference9th IEEE International Conference on Big Knowledge, ICBK 2018
Country/TerritorySingapore
CitySingapore
Period11/17/18 → 11/18/18

ASJC Scopus Subject Areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management

Keywords

  • Diversification
  • Motifs
  • Sampling
  • Time series

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