Chaotic Time Series Prediction Based on Local-Region Multi-steps Forecasting Model

摘要

Large computational quantity and cumulative error are main shortcomings of add- weighted one-rank local-region single-step method for multi-steps prediction of chaotic time series. A local-region multi-steps forecasting model based on phase-space reconstruction is presented for chaotic time series prediction, including add-weighted one-rank local-region multi-steps forecasting model and RBF neural network multi-steps forecasting model. Simulation results from several typical chaotic time series demonstrate that both of these models are effective for multi-steps prediction of chaotic time series.