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The delay vectors of nonlinear time series are mapped into a second-order space and the intrinsic dimension of the underlying dynamics is estimated by the saturation of the nonvanishing eigenvalues of the covariant matrix as the embedding dimension increases. This method needs less data and has certain noise resistant degree.
This paper presents a new method, called multiple temporal pattern recognition (MTPR), that is capable of detecting multiple temporal patterns for characterizing and predicting events of interest in the time-evolving system data. The MTPR method embeds time series data into multiple phase spaces with various dimensions and time delays. Then it clusters the embedded data to detect the preliminary temporal...
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