Chaotic time series multi-step direct prediction with partial least squares regression |
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Authors: | Liu Zunxiong Liu Jianhui |
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Affiliation: | School of Information Engineering,Huadong Jiaotong Univ.,Nachang 330013,P.R.China |
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Abstract: | Considering chaotic time series multi-step prediction,multi-step direct prediction model based on partial least squares(PLS)is proposed in this article,where PLS,the method for predicting a set of dependent variables forming a large set of predictors,is used to model the dynamic evolution between the space points and the corresponding future points.The model can eliminate error accumulation with the common single-step local model algorithm,and refrain from the high multi-collinearity problem in the reconstructed state space with the increase of embedding dimension.Simulation predictions are done on the Mackey-Glass chaotic time series with the model.The satisfying prediction accuracy is obtained and the model efficiency verified.In the experiments,the number of extracted components in PLS is set with Cross-validation procedure. |
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Keywords: | chaotic series prediction multi-step local model partial least squares. |
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