Classification of Time Series Data with Nonlinear Similarity Measures

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Abstract
We address the problem of unsupervised classification of time series recordings. The aim is to form groups of sequences or segments of a longer sequence which show similar dynamical behavior. Several measures of similarity between two time series are considered. Groups or clusters are then formed by minimizing a suitable cost function using simulated annealing. Apart from the classification of systems, the method can be applied to the monitoring of abrupt or continuous changes in nonstationary signals. We further discuss the inclusion of supervision and propose the use of similarity measures in tests for nonlinearity based on surrogate data.