Classification of Multivariate Time Series of Arbitrary Nature Based on the \(\epsilon \)-Complexity Theory
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.
KeywordsMultivariate time series Classification Epsilon-complexity
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