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Stochastic configuration network based on improved whale optimization algorithm for nonstationary time series prediction

Author

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  • Zi‐yu Chen
  • Fei Xiao
  • Xiao‐kang Wang
  • Min‐hui Deng
  • Jian‐qiang Wang
  • Jun‐Bo Li

Abstract

The stochastic configuration network (SCN), a type of randomized learning algorithm, can solve the infeasible problem in random vector functional link (RVFL) by establishing a supervisory mechanism. The advantages of fast learning, convergence and not easily falling into local optima make SCN popular. However, the prediction effect of SCN is affected by the parameter settings and the nonstationarity of input data. In this paper, a hybrid model based on variational mode decomposition (VMD), improved whale optimization algorithm (IWOA), and SCN is proposed. The SCN will predict relatively stable data after decomposition by VMD, and parameters of SCN are optimized by IWOA. The IWOA diversifies the initial population by employing logistic chaotic map based on bit reversal and improves the search ability by using Lévy flight. The exploration and exploitation of IWOA are superior to those of other optimization algorithms in multiple benchmark functions and CEC2020. Moreover, the proposed model is applied to the prediction of the nonstationary wind speeds in four seasons. We evaluate the performance of the proposed model using four evaluation indicators. The results show that the R2 of the proposed model under four seasons are more than 0.999, and the root mean square error, mean absolute error, and symmetric mean absolute percentage error are less than 0.3, 0.17, and 13%, respectively, which are almost 1/10, 1/10, and 1/4 those of SCN, respectively.

Suggested Citation

  • Zi‐yu Chen & Fei Xiao & Xiao‐kang Wang & Min‐hui Deng & Jian‐qiang Wang & Jun‐Bo Li, 2022. "Stochastic configuration network based on improved whale optimization algorithm for nonstationary time series prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1458-1482, November.
    • Handle: RePEc:wly:jforec:v:41:y:2022:i:7:p:1458-1482
    DOI: 10.1002/for.2870
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    References listed on IDEAS

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    1. Chitsazan, Mohammad Amin & Sami Fadali, M. & Trzynadlowski, Andrzej M., 2019. "Wind speed and wind direction forecasting using echo state network with nonlinear functions," Renewable Energy, Elsevier, vol. 131(C), pages 879-889.
    2. Liu, Hui & Mi, Xiwei & Li, Yanfei, 2018. "An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm," Renewable Energy, Elsevier, vol. 123(C), pages 694-705.
    3. Ebru Pekel Özmen & Tuncay Özcan, 2020. "Diagnosis of diabetes mellitus using artificial neural network and classification and regression tree optimized with genetic algorithm," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 661-670, July.
    4. Zheng‐Ling Yang & Ya‐Di Liu & Xin‐Shan Zhu & Xi Chen & Jun Zhang, 2016. "Removing Forecasting Errors with White Gaussian Noise after Square Root Transformation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(8), pages 741-750, December.
    5. Saeed, Muhammad Abid & Ahmed, Zahoor & Zhang, Weidong, 2020. "Wind energy potential and economic analysis with a comparison of different methods for determining the optimal distribution parameters," Renewable Energy, Elsevier, vol. 161(C), pages 1092-1109.
    6. Andrea Bucci, 2020. "Cholesky–ANN models for predicting multivariate realized volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 865-876, September.
    7. Xiaojia Ye & Wei Liu & Hong Li & Mingjing Wang & Chen Chi & Guoxi Liang & Huiling Chen & Hailong Huang & Ramon Costa-Castelló, 2021. "Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification," Complexity, Hindawi, vol. 2021, pages 1-23, February.
    8. Emil Kraft & Dogan Keles & Wolf Fichtner, 2020. "Modeling of frequency containment reserve prices with econometrics and artificial intelligence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1179-1197, December.
    9. Svetlana Borovkova & Ioannis Tsiamas, 2019. "An ensemble of LSTM neural networks for high‐frequency stock market classification," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(6), pages 600-619, September.
    10. Liu, Hui & Mi, Xiwei & Li, Yanfei & Duan, Zhu & Xu, Yinan, 2019. "Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression," Renewable Energy, Elsevier, vol. 143(C), pages 842-854.
    11. Yinghao Chen & Xiaoliang Xie & Tianle Zhang & Jiaxian Bai & Muzhou Hou, 2020. "A deep residual compensation extreme learning machine and applications," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 986-999, September.
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