A Second-Order Mapping PCA Based Intrinsic Dimension Estimate for Nonlinear Time Series

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.