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Robust Online Support Vector Regression with Truncated ε -Insensitive Pinball Loss

Author

Listed:
  • Xian Shan

    (College of Science, China University of Petroleum, Qingdao 266580, China)

  • Zheshuo Zhang

    (College of Science, China University of Petroleum, Qingdao 266580, China)

  • Xiaoying Li

    (College of Science, China University of Petroleum, Qingdao 266580, China)

  • Yu Xie

    (College of Science, China University of Petroleum, Qingdao 266580, China)

  • Jinyu You

    (College of Science, China University of Petroleum, Qingdao 266580, China)

Abstract

Advances in information technology have led to the proliferation of data in the fields of finance, energy, and economics. Unforeseen elements can cause data to be contaminated by noise and outliers. In this study, a robust online support vector regression algorithm based on a non-convex asymmetric loss function is developed to handle the regression of noisy dynamic data streams. Inspired by pinball loss, a truncated ε -insensitive pinball loss (TIPL) is proposed to solve the problems caused by heavy noise and outliers. A TIPL-based online support vector regression algorithm (TIPOSVR) is constructed under the regularization framework, and the online gradient descent algorithm is implemented to execute it. Experiments are performed using synthetic datasets, UCI datasets, and real datasets. The results of the investigation show that in the majority of cases, the proposed algorithm is comparable, or even superior, to the comparison algorithms in terms of accuracy and robustness on datasets with different types of noise.

Suggested Citation

  • Xian Shan & Zheshuo Zhang & Xiaoying Li & Yu Xie & Jinyu You, 2023. "Robust Online Support Vector Regression with Truncated ε -Insensitive Pinball Loss," Mathematics, MDPI, vol. 11(3), pages 1-22, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:709-:d:1051951
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    References listed on IDEAS

    as
    1. Wu, Jinran & Wang, You-Gan & Tian, Yu-Chu & Burrage, Kevin & Cao, Taoyun, 2021. "Support vector regression with asymmetric loss for optimal electric load forecasting," Energy, Elsevier, vol. 223(C).
    2. Amir Safari, 2014. "An e–E-insensitive support vector regression machine," Computational Statistics, Springer, vol. 29(6), pages 1447-1468, December.
    3. Wei, Nan & Yin, Lihua & Li, Chao & Li, Changjun & Chan, Christine & Zeng, Fanhua, 2021. "Forecasting the daily natural gas consumption with an accurate white-box model," Energy, Elsevier, vol. 232(C).
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