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

Chaw Thet Zan, Hayato Yamana

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings
編集者Gabriele Anderst-Kotsis, Matthias Steinbauer, Ismail Khalil, Maria Indrawan-Santiago, Ivan Luiz Salvadori
出版社Association for Computing Machinery
ページ49-57
ページ数9
ISBN(電子版)9781450352994
DOI
出版ステータスPublished - 2017 12月 4
イベント19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Salzburg, Austria
継続期間: 2017 12月 42017 12月 6

出版物シリーズ

名前ACM International Conference Proceeding Series

Other

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

ASJC Scopus subject areas

  • ソフトウェア
  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ ネットワークおよび通信

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