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Time Series Classification Using Gaussian Mixture Models Of Reconstructed Phase Spaces, Richard J. Povinelli, Michael T. Johnson, Andrew C. Lindgren, Jinjin Ye

Electrical and Computer Engineering Faculty Research and Publications

A new signal classification approach is presented that is based upon modeling the dynamics of a system as they are captured in a reconstructed phase space. The modeling is done using full covariance Gaussian mixture models of time domain signatures, in contrast with current and previous work in signal classification that is typically focused on either linear systems analysis using frequency content or simple nonlinear machine learning models such as artificial neural networks. The proposed approach has strong theoretical foundations based on dynamical systems and topological theorems, resulting in a signal reconstruction, which is asymptotically guaranteed to be a complete …