An Improved Local Weighted Linear Prediction Model for Chaotic Time Series

Previous research working on local prediction state with some unsuitable neighbor points (such as false points and pseudo-false neighbor points) are the main source of errors of local prediction and these unsuitable neighbors cannot be eliminated entirely. Therefore, an improved local weighted linear prediction model based on local integrated correlation, which can reduce the influence of the residual unsuitable neighbors, is proposed to predict chaotic time series in our study. Simulation results show that the performance of the improved model is superior to the other local prediction models in the prediction of chaotic time series without and with additive white Gaussian noise.