A New Complexity Measure for Time Series Analysis and Classification
Publication Type:Journal Article
Source:The European Physical Journal Special Topics, Springer Berlin Heidelberg, Volume 222, Number 3-4, p.847–860 (2013)
Complexity measures are used in a number of applications including 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.
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