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A prediction method based on wavelet transform and multiple models fusion for chaotic time series

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  • Zhongda, Tian
  • Shujiang, Li
  • Yanhong, Wang
  • Yi, Sha

Abstract

In order to improve the prediction accuracy of chaotic time series, a prediction method based on wavelet transform and multiple models fusion is proposed. The chaotic time series is decomposed and reconstructed by wavelet transform, and approximate components and detail components are obtained. According to different characteristics of each component, least squares support vector machine (LSSVM) is used as predictive model for approximation components. At the same time, an improved free search algorithm is utilized for predictive model parameters optimization. Auto regressive integrated moving average model (ARIMA) is used as predictive model for detail components. The multiple prediction model predictive values are fusion by Gauss–Markov algorithm, the error variance of predicted results after fusion is less than the single model, the prediction accuracy is improved. The simulation results are compared through two typical chaotic time series include Lorenz time series and Mackey–Glass time series. The simulation results show that the prediction method in this paper has a better prediction.

Suggested Citation

  • Zhongda, Tian & Shujiang, Li & Yanhong, Wang & Yi, Sha, 2017. "A prediction method based on wavelet transform and multiple models fusion for chaotic time series," Chaos, Solitons & Fractals, Elsevier, vol. 98(C), pages 158-172.
    • Handle: RePEc:eee:chsofr:v:98:y:2017:i:c:p:158-172
    DOI: 10.1016/j.chaos.2017.03.018
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    Cited by:

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    2. Ravi Kumar, Rajagounder, 2022. "Adaptive wavelet output regulation for nonlinear systems," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    3. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    4. Fu, Ke & Li, He & Deng, Pengfei, 2022. "Chaotic time series prediction using DTIGNet based on improved temporal-inception and GRU," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).

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