A new method of determining the optimal embedding dimension based on nonlinear prediction

A new method is proposed to determine the optimal embedding dimension from a scalar time series in this paper. This method determines the optimal embedding dimension by optimizing the nonlinear autoregressive prediction model parameterized by the embedding dimension and the nonlinear degree. Simulation results show the effectiveness of this method. And this method is applicable to a short time series, stable to noise, computationally efficient, and without any purposely introduced parameters.     

蠓的线性判别

本文在没有任何假设的条件下，建立了一个对蠓分类的数学模型．所用理论工具，计算工具都很简单．且经反馈检验．对已知种类的蠓模型判断完全正确．