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

10 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
EditorsMaria Indrawan-Santiago, Gabriele Anderst-Kotsis, Matthias Steinbauer, Ismail Khalil
PublisherAssociation for Computing Machinery
Pages72-80
Number of pages9
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

Publication series

NameACM International Conference Proceeding Series

Other

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

Keywords

  • Classi-cation
  • Dimension reduction
  • Statistical features
  • 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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