摘要In the reconstructed phase space, a novel local linear prediction model is proposed to predict chaotic time series. The parameters of the proposed model take the values that are different from those of the phase space reconstruction. We propose a criterion based on prediction error to determine the optimal parameters of the proposed model. The simulation results show that the proposed model can effectively make one-step and multi-step prediction for chaotic time series, and the one-step and multi-step prediction accuracy of the proposed model is superior to that of the traditional local linear prediction.
Abstract：In the reconstructed phase space, a novel local linear prediction model is proposed to predict chaotic time series. The parameters of the proposed model take the values that are different from those of the phase space reconstruction. We propose a criterion based on prediction error to determine the optimal parameters of the proposed model. The simulation results show that the proposed model can effectively make one-step and multi-step prediction for chaotic time series, and the one-step and multi-step prediction accuracy of the proposed model is superior to that of the traditional local linear prediction.
MENG Qing-Fang;PENG Yu-Hua;LIU Yun-Xia;SUN Wei-Feng. Analyses of Optimal Embedding Dimension and Delay for Local Linear Prediction Model[J]. 中国物理快报, 2007, 24(7): 1833-1836.
MENG Qing-Fang, PENG Yu-Hua, LIU Yun-Xia, SUN Wei-Feng. Analyses of Optimal Embedding Dimension and Delay for Local Linear Prediction Model. Chin. Phys. Lett., 2007, 24(7): 1833-1836.
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2007, Vol. 24 
