Discovering sub-patterns from time series using a normalized cross-match algorithm

Gong, X; Fong, S; Wong, RK; Mohammed, S; Fiaidhi, J; Vasilakos, AV

Discovering sub-patterns from time series using a normalized cross-match algorithm

Gong, X Fong, S Wong, RK Mohammed, S Fiaidhi, J Vasilakos, AV

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Journal of Supercomputing, 2016, 72, (10), pp. 3850-3867
Issue Date:: 2016-10-01

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Field	Value	Language
dc.contributor.author	Gong, X
dc.contributor.author	Fong, S
dc.contributor.author	Wong, RK
dc.contributor.author	Mohammed, S
dc.contributor.author	Fiaidhi, J
dc.contributor.author	Vasilakos, AV
dc.date.accessioned	2022-08-12T02:31:05Z
dc.date.available	2022-08-12T02:31:05Z
dc.date.issued	2016-10-01
dc.identifier.citation	Journal of Supercomputing, 2016, 72, (10), pp. 3850-3867
dc.identifier.issn	0920-8542
dc.identifier.issn	1573-0484
dc.identifier.uri	http://hdl.handle.net/10453/160002
dc.description.abstract	Time series data stream mining has attracted considerable research interest in recent years. Pattern discovery is a challenging problem in time series data stream mining. Because the data update continuously and the sampling rates may be different, dynamic time warping (DTW)-based approaches are used to solve the pattern discovery problem in time series data streams. However, the naive form of the DTW-based approach is computationally expensive. Therefore, Toyoda proposed the CrossMatch (CM) approach to discover the patterns between two time series data streams (sequences), which requires only O(n) time per data update, where n is the length of one sequence. CM, however, does not support normalization, which is required for some kinds of sequences (e.g. stock prices, ECG data). Therefore, we propose a normalized-CrossMatch approach that extends CM to enforce normalization while maintaining the same performance capabilities.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Journal of Supercomputing
dc.relation.isbasedon	10.1007/s11227-016-1632-z
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing
dc.subject.classification	Distributed Computing
dc.title	Discovering sub-patterns from time series using a normalized cross-match algorithm
dc.type	Journal Article
utslib.citation.volume	72
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-12T02:31:04Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	72
utslib.citation.issue	10

Abstract:

Time series data stream mining has attracted considerable research interest in recent years. Pattern discovery is a challenging problem in time series data stream mining. Because the data update continuously and the sampling rates may be different, dynamic time warping (DTW)-based approaches are used to solve the pattern discovery problem in time series data streams. However, the naive form of the DTW-based approach is computationally expensive. Therefore, Toyoda proposed the CrossMatch (CM) approach to discover the patterns between two time series data streams (sequences), which requires only O(n) time per data update, where n is the length of one sequence. CM, however, does not support normalization, which is required for some kinds of sequences (e.g. stock prices, ECG data). Therefore, we propose a normalized-CrossMatch approach that extends CM to enforce normalization while maintaining the same performance capabilities.

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http://hdl.handle.net/10453/160002