Classification of Multivariate Time Series of Arbitrary Nature Based on the ε-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 Holder 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.