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

Bangzhu Zhu, Jingyi Zhang, Chunzhuo Wan, Julien Chevallier and Ping Wang

Journal of Forecasting, 2023, vol. 42, issue 4, 741-755

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.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1002/for.2916

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:42:y:2023:i:4:p:741-755

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().