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A machine learning approach to predict turning points for chaotic time series was proposed through incorporating chaotic analysis into ensemble artificial neural network (ANN) modeling. The EM-like parameter learning algorithm for ensemble ANN model was presented. We then gave a new GA-based threshold optimization procedure using out-of-sample validation. The proposed approach was demonstrated on the benchmark chaotic time series like Mackey-Glass system. Our experimental results show significant improvement in performance over ANN model alone.