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

Chaw Thet Zan; Hayato Yamana

doi:10.1145/3011141.3011146

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

Chaw Thet Zan, Hayato Yamana

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 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 language	English
Title of host publication	18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings
Publisher	Association for Computing Machinery
Pages	72-80
Number of pages	9
Volume	Part F126325
ISBN (Electronic)	9781450348072
DOIs	https://doi.org/10.1145/3011141.3011146
Publication status	Published - 2016 Nov 28
Event	18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Singapore, Singapore Duration: 2016 Nov 28 → 2016 Nov 30

Other

Other	18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016
Country	Singapore
City	Singapore
Period	16/11/28 → 16/11/30

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

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An improved symbolic aggregate approximation distance measure based on its statistical features. / Thet Zan, Chaw; Yamana, Hayato.

18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings. Vol. Part F126325 Association for Computing Machinery, 2016. p. 72-80.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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, Association for Computing Machinery, pp. 72-80, 18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016, Singapore, Singapore, 16/11/28. https://doi.org/10.1145/3011141.3011146

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title = "An improved symbolic aggregate approximation distance measure based on its statistical features",

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).",

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AB - 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).

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

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