An improved symbolic aggregate approximation distance measure based on its statistical features

Chaw Thet Zan, Hayato Yamana

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

    6 Citations (Scopus)

    Abstract

    The challenges in effcient data representation and similarity measures on massive amounts of time series have enormous impact on many applications. This paper addresses an improvement on Symbolic Aggregate approXimation (SAX), is one of the effcient representations for time series mining. Because SAX represents its symbols by the average (mean) value of a segment with the assumption of Gaussian distribution, it is insuficient to serve the entire deterministic information and causes sometimes incorrect results in time series classiffcation. In this work, SAX representation and distance measure is improved with the addition of another moment of the prior distribution, standard deviation; SAX SD is proposed. We provide comprehensive analysis for the proposed SAX SD and conrm both the highest classi-fication accuracy and the highest dimensionality reduction ratio on University of California, Riverside (UCR) datasets in comparison to state of the art methods such as SAX, Extended SAX (ESAX) and SAX Trend Distance (SAX TD).

    Original languageEnglish
    Title of host publication18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings
    PublisherAssociation for Computing Machinery
    Pages72-80
    Number of pages9
    VolumePart F126325
    ISBN (Electronic)9781450348072
    DOIs
    Publication statusPublished - 2016 Nov 28
    Event18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Singapore, Singapore
    Duration: 2016 Nov 282016 Nov 30

    Other

    Other18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016
    CountrySingapore
    CitySingapore
    Period16/11/2816/11/30

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    Keywords

    • Classi-cation
    • Dimension reduction
    • Statistical features
    • Symbolic representation
    • Time series

    ASJC Scopus subject areas

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

    Cite this

    Thet Zan, C., & Yamana, H. (2016). An improved symbolic aggregate approximation distance measure based on its statistical features. In 18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings (Vol. Part F126325, pp. 72-80). Association for Computing Machinery. https://doi.org/10.1145/3011141.3011146