Classification of Multivariate Time Series of Arbitrary Nature Based on the \(\epsilon \)-Complexity Theory

  • Boris Darkhovsky
  • Alexandra Piryatinska
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 231)


The problem of classification of relatively short multivariate time series generated by different mechanisms (stochastic, deterministic or mixed) is considered. We generalize our theory of the \(\epsilon \)-complexity, which was developed for scalar continuous functions, to the case of vector-valued functions from Hölder class. The methodology for classification of multivariate time series based on the \(\epsilon \)-complexity parameters is proposed. The results on classification of simulated data and real data (EEG records of alcoholic and control groups) are provided.


Multivariate time series Classification Epsilon-complexity 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Systems AnalysisFRC CSC RAS, Higher School of EconomicsMoscowRussia
  2. 2.San Francisco State UniversityCAUSA

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