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An evolutionary cost‐sensitive support vector machine for carbon price trend forecasting

Author

Listed:
  • Bangzhu Zhu
  • Jingyi Zhang
  • Chunzhuo Wan
  • Julien Chevallier
  • Ping Wang

Abstract

This paper aims at the imbalanced characteristics and proposes a novel evolutionary cost‐sensitive support vector machine (CSSVM) by integrating cost‐sensitive learning, support vector machine, and genetic algorithm for carbon price trend prediction. First, carbon price trend prediction is converted into a binary‐class prediction problem for CSSVM, in which a higher misclassification cost is imposed on the minority samples. In comparison, a more negligible misclassification cost is imposed on most samples. Second, a genetic algorithm (GA) is used to optimize all parameters of CSSVM synchronously. Taking Beijing, Hubei, and Guangdong carbon markets as samples, the empirical results show that the proposed model has a higher classification accuracy and lower misclassification costs compared with other popular prediction models. Furthermore, the sensitivity analysis verifies that the proposed approach is robust.

Suggested Citation

  • Bangzhu Zhu & Jingyi Zhang & Chunzhuo Wan & Julien Chevallier & Ping Wang, 2023. "An evolutionary cost‐sensitive support vector machine for carbon price trend forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 741-755, July.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:4:p:741-755
    DOI: 10.1002/for.2916
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    References listed on IDEAS

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