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The prediction of chaotic time series is performed by least square support vector machine (LS-SVM) based on particle swarm optimization (PSO). The main objective of this approach is to increase the accuracy of the chaotic time series prediction. For the generation performance of LS-SVM depending on a good setting of its parameters, PSO is adopted to choose the global optimum parameters of LS-SVM automatically. The proposed model is applied to the three important chaotic time series including Mackey-Glass time series, Lorenz time series and Henon time series. The simulation results prove the feasibility and effectiveness of the method.