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

Chaw Thet Zan, Hayato Yamana

    研究成果: Conference contribution

    10 被引用数 (Scopus)

    抄録

    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).

    本文言語English
    ホスト出版物のタイトル18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings
    出版社Association for Computing Machinery
    ページ72-80
    ページ数9
    Part F126325
    ISBN(電子版)9781450348072
    DOI
    出版ステータスPublished - 2016 11 28
    イベント18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Singapore, Singapore
    継続期間: 2016 11 282016 11 30

    Other

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

    ASJC Scopus subject areas

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

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