A new complexity measure for time series analysis and classification

Nagaraj, N. and Balasubramanian, K. and Dey, S. (2013) A new complexity measure for time series analysis and classification. European Physical Journal - Special Topics, 222. pp. 847-860. ISSN 1951-6355

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    Abstract: Complexity measures are used in a number of applications such as extraction of information from data such as ecological time series, detection of non-random structure in biomedical signals, testing of random number generators, language recognition and authorship attribution etc. Different complexity measures proposed in the literature like Shannon entropy, Relative entropy, Lempel-Ziv, Kolmogrov and Algorithmic complexity are mostly ineffective in analyzing short sequences that are further corrupted with noise. To address this problem, we propose a new complexity measure ETC and define it as the “Effort To Compress
    Item Type: Article
    Subjects: School of Natural and Engineering Sciences > Mathematical Modeling
    Divisions: Schools > Natural Sciences and Engineering
    Date Deposited: 11 Nov 2013 05:50
    Last Modified: 08 May 2015 10:08
    URI: http://eprints.nias.res.in/id/eprint/398

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