IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v41y2022i7p1458-1482.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.2870
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.2870?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Zicheng & Gao, Ruobin & Wang, Piao & Chen, Huayou, 2023. "A new perspective on air quality index time series forecasting: A ternary interval decomposition ensemble learning paradigm," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    2. Shang, Zhihao & He, Zhaoshuang & Chen, Yao & Chen, Yanhua & Xu, MingLiang, 2022. "Short-term wind speed forecasting system based on multivariate time series and multi-objective optimization," Energy, Elsevier, vol. 238(PC).
    3. Li, Yanfei & Shi, Huipeng & Han, Fengze & Duan, Zhu & Liu, Hui, 2019. "Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy," Renewable Energy, Elsevier, vol. 135(C), pages 540-553.
    4. García, Irene & Huo, Stella & Prado, Raquel & Bravo, Lelys, 2020. "Dynamic Bayesian temporal modeling and forecasting of short-term wind measurements," Renewable Energy, Elsevier, vol. 161(C), pages 55-64.
    5. Ren, Weijie & Li, Baisong & Han, Min, 2020. "A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    6. Mojtaba Qolipour & Ali Mostafaeipour & Mohammad Saidi-Mehrabad & Hamid R Arabnia, 2019. "Prediction of wind speed using a new Grey-extreme learning machine hybrid algorithm: A case study," Energy & Environment, , vol. 30(1), pages 44-62, February.
    7. Papul Changmai & Sunil Deka & Shashank Kumar & Thanikanti Sudhakar Babu & Belqasem Aljafari & Benedetto Nastasi, 2022. "A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters," Energies, MDPI, vol. 15(19), pages 1-20, September.
    8. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    9. Wei Sun & Ming Duan, 2019. "Analysis and Forecasting of the Carbon Price in China’s Regional Carbon Markets Based on Fast Ensemble Empirical Mode Decomposition, Phase Space Reconstruction, and an Improved Extreme Learning Machin," Energies, MDPI, vol. 12(2), pages 1-27, January.
    10. Ruiheng Li & Yi Di & Qiankun Zuo & Hao Tian & Lu Gan, 2023. "Enhanced Whale Optimization Algorithm for Improved Transient Electromagnetic Inversion in the Presence of Induced Polarization Effects," Mathematics, MDPI, vol. 11(19), pages 1-20, October.
    11. Hao Wang & Chen Peng & Bolin Liao & Xinwei Cao & Shuai Li, 2023. "Wind Power Forecasting Based on WaveNet and Multitask Learning," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    12. Jianzhong Zhou & Han Liu & Yanhe Xu & Wei Jiang, 2018. "A Hybrid Framework for Short Term Multi-Step Wind Speed Forecasting Based on Variational Model Decomposition and Convolutional Neural Network," Energies, MDPI, vol. 11(9), pages 1-18, August.
    13. Menglu Li & Wei Wang & Gejirifu De & Xionghua Ji & Zhongfu Tan, 2018. "Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm," Energies, MDPI, vol. 11(9), pages 1-15, September.
    14. Lucien Boulet, 2021. "Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs," Papers 2109.01044, arXiv.org.
    15. Tang, Zhenhao & Zhao, Gengnan & Ouyang, Tinghui, 2021. "Two-phase deep learning model for short-term wind direction forecasting," Renewable Energy, Elsevier, vol. 173(C), pages 1005-1016.
    16. Yuanrong Wang & Yinsen Miao & Alexander CY Wong & Nikita P Granger & Christian Michler, 2023. "Domain-adapted Learning and Interpretability: DRL for Gas Trading," Papers 2301.08359, arXiv.org, revised Sep 2023.
    17. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    18. Xue Gong & Weiguo Zhang & Yuan Zhao & Xin Ye, 2023. "Forecasting stock volatility with a large set of predictors: A new forecast combination method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1622-1647, November.
    19. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    20. Heilmann, Erik, 2023. "The impact of transparency policies on local flexibility markets in electric distribution networks," Utilities Policy, Elsevier, vol. 83(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:41:y:2022:i:7:p:1458-1482. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.