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
    PublisherAssociation for Computing Machinery
    Pages49-57
    Number of pages9
    VolumePart F134476
    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

    Other

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

    Fingerprint

    Time series
    Experiments

    Keywords

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

    ASJC Scopus subject areas

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

    Cite this

    Zan, C. T., & Yamana, H. (2017). A variable-length motifs discovery method in time series using hybrid approach. In 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings (Vol. Part F134476, pp. 49-57). Association for Computing Machinery. https://doi.org/10.1145/3151759.3151781

    A variable-length motifs discovery method in time series using hybrid approach. / Zan, Chaw Thet; Yamana, Hayato.

    19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings. Vol. Part F134476 Association for Computing Machinery, 2017. p. 49-57.

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

    Zan, CT & Yamana, H 2017, A variable-length motifs discovery method in time series using hybrid approach. in 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings. vol. Part F134476, Association for Computing Machinery, pp. 49-57, 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017, Salzburg, Austria, 17/12/4. https://doi.org/10.1145/3151759.3151781
    Zan CT, Yamana H. A variable-length motifs discovery method in time series using hybrid approach. In 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings. Vol. Part F134476. Association for Computing Machinery. 2017. p. 49-57 https://doi.org/10.1145/3151759.3151781
    Zan, Chaw Thet ; Yamana, Hayato. / A variable-length motifs discovery method in time series using hybrid approach. 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings. Vol. Part F134476 Association for Computing Machinery, 2017. pp. 49-57
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