Prediction of chaotic time series based on the recurrent predictor neural network
IEEE Transactions on Signal Processing
Prediction of noisy chaotic time series using an optimal radial basis function neural network
IEEE Transactions on Neural Networks
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In order to obtain the effective input vector for the prediction of multivariate time series, a generic phase space reconstruction method combining classical reconstruction technology with reduction theory of rough sets was proposed. Firstly, the embedding dimension was determined by minimizing the mean one-step prediction error and the original reconstruction phase space was obtained. Then, the original decision-table with redundant embedding dimensions for multivariate time series was set up and the RS reduction was used to delete the redundant dimensions and irrelevant variables and to reconstruct the generic phase space. Finally, the samples were extracted according to generic reconstruction results to identify the parameters of prediction model. Verification results show that the developed reconstruction method leads better generalization ability for the prediction model and it is feasible and worthwhile for application.