Prediction of chaotic time series based on modified minimax probability machine regression |
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| Authors: | Sun Jian-Cheng |
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| Affiliation: | School of Electronics, Jiangxi University of Finance and Economics, Nanchang 330013, China |
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| Abstract: | Long-term prediction of chaotic time series is very difficult, for the chaos restricts predictability. In this papera new method is studied to model and predict chaotic time series based on minimax probability machine regression(MPMR). Since the positive global Lyapunov exponents lead the errors to increase exponentially in modelling thechaotic time series, a weighted term is introduced to compensate a cost function. Using mean square error (MSE) andabsolute error (AE) as a criterion, simulation results show that the proposed method is more effective and accurate formultistep prediction. It can identify the system characteristics quite well and provide a new way to make long-termpredictions of the chaotic time series. |
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| Keywords: | minimax probability machine regression (MPMR) time series prediction chaos |
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