A variable-length motifs discovery method in time series using hybrid approach

Chaw Thet Zan, Hayato Yamana

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

Abstract

Discovery of repeated patterns, known as motifs, from long time series is essential for providing hidden knowledge to real-world applications like medical, financial and weather analysis. Motifs can be discovered on raw time series directly or on their transformed abstract representation alternatively. Most of time series motif discovery methods require predefined motif length, which results in long execution time because we have to vary the length to discover motifs with different lengths. To solve the problem, we propose an efficient method for discovering variable length motifs in combination of approximate method with exact verification. First, symbolic representation is adopted to discover motifs roughly followed by exact examination of the found motifs with original real-valued data to achieve fast and exact discovery. The experiments show that our proposed method successfully discovered significant motifs efficiently in comparison with state-of-the-art methods: MK and SBF.

Original languageEnglish
Title of host publication19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings
EditorsGabriele Anderst-Kotsis, Matthias Steinbauer, Ismail Khalil, Maria Indrawan-Santiago, Ivan Luiz Salvadori
PublisherAssociation for Computing Machinery
Pages49-57
Number of pages9
ISBN (Electronic)9781450352994
DOIs
Publication statusPublished - 2017 Dec 4
Event19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Salzburg, Austria
Duration: 2017 Dec 42017 Dec 6

Publication series

NameACM International Conference Proceeding Series

Other

Other19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017
CountryAustria
CitySalzburg
Period17/12/417/12/6

Keywords

  • Frequent pattern mining
  • Motif
  • Symbolic representation
  • Time series

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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