关键词: 混沌时间序列, 贝叶斯网络, 预测, 相空间重构

Abstract: Chaotic time series prediction is a very important research area and hotspot of chaotic theory；it is widely used in weather, electric power, economy, commutation etc. However, because of the high complexity of the nonlinear character of chaotic time series, it is hard to make quantitative research in theory. This paper uses the advantages of Bayesian Networks（BNs） in dealing the uncertainty together with the theory of phase-space reconfiguration to build a nonlinear prediction model for the prediction of chaotic time series. In this way, it can analyze the dynamic character and realize the prediction. The experimental results show that this prediction model has good predictability and stability, and overcomes the over fitting effectively.

Key words: chaotic time series, Bayesian Networks（BNs）, prediction, phase-space reconfiguration