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Discovering sub-patterns from time series using a normalized cross-match algorithm
Department of Computer and Information Science, University of Macau.
Department of Computer and Information Science, University of Macau.
School of Computer Science and Engineering, University of New South Wales, Sydney.
Department of Computer Science, Lakehead University, Thunder Bay.
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Number of Authors: 62016 (English)In: Journal of Supercomputing, ISSN 0920-8542, E-ISSN 1573-0484, Vol. 72, no 10, p. 3850-3867Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2016. Vol. 72, no 10, p. 3850-3867
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-10155DOI: 10.1007/s11227-016-1632-zISI: 000385417400010Scopus ID: 2-s2.0-84957541738Local ID: 8e8c7b21-2805-442c-8417-86d015602267OAI: oai:DiVA.org:ltu-10155DiVA, id: diva2:983095
Note

Validerad; 2016; Nivå 2; 2016-11-09 (rokbeg)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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Vasilakos, Athanasios

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  • apa
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