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A new method is proposed to inverse normalization data of hidden variables in a dynamical system by embedding a time series in multidimensional spaces and applying a normalization analysis to the conditional probability density of points in the reconstructed phase spaces. The method is robust in the application to Lorenz system and 4-dimensional Rossler system by testing quantitatively and qualitatively the correlation coefficient between inverse data and original data in time domain and in frequency domain, respectively. By applying the method to analyzing the South China Sea data, the normalization data of wind speed is extracted from the sea surface temperature time series.
A new method is proposed to inverse normalization data of hidden variables in a dynamical system by embedding a time series in multidimensional spaces and applying a normalization analysis to the conditional probability density of points in the reconstructed phase spaces. The method is robust in the application to Lorenz system and 4-dimensional Rossler system by testing quantitatively and qualitatively the correlation coefficient between inverse data and original data in time domain and in frequency domain, respectively. By applying the method to analyzing the South China Sea data, the normalization data of wind speed is extracted from the sea surface temperature time series.