Time Series Classification by Reconstruction and Approximation of Phase Space

Petr Podhorský and Miroslav Skrbek


Machine Learning, phase space, reconstruction, approximation, genetic algorithm, classification


In this paper, a new algorithm for time series classification is proposed. It is based on comparison of approximated phase spaces reconstructed from data for classification. The reconstruction process embeds advanced feature selection controlled by a genetic algorithm providing the most significant subset of components of phase vectors with respect to maximization of separability of different data classes. Phase space is approximated by a set of Gaussian kernels located in a regular multidimensional grid. Phase spaces are compared point by point within the grid. The proposed algorithm has been tested on artificial data to study its behaviour and properties, and on real data of human motion measured using 3-axis accelerometer.

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