Online chaotic time-series´ prediction using EKF, UKF and GPF

This work uses the weights and network output of neural networks (NN) as state equation and measurement equation for chaotic time-series´ prediction to obtain the linear state transition equation which is different from the previous filtering methods based chaotic time-series´ prediction, and the prediction results of chaotic time series is represented by the predicted measurement value. An efficient algorithm with continuous update prediction scheme for chaotic time-series is suggested. This scheme is tested using simulated data based on the EKF, UKF and Gaussian particle filtering (GPF), respectively. Simulation results have proved that the GPF is superior to EKF and UKF for Mackey-Glass time-series´ prediction with the proposed model proposed in this paper.