Phase Space Prediction Model Based on the Chaotic Attractor

A new prediction technique is proposed for chaotic time series. The usefulness of the technique is that it removes some false neighbouring points which are not suitable for the local estimation of the dynamics systems. We use a feedforward neural network to approximate the local dominant Lyapunov exponent, and choose the neighbouring points by the exponent. The model is tested for the convection amplitude of the Lorenz model, and the results indicate that this prediction technique can improve the prediction of chaotic time series.