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

Bangzhu Zhu, Jingyi Zhang, Chunzhuo Wan, Julien Chevallier and Ping Wang
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Julien Chevallier: LED - Laboratoire d'Economie Dionysien - UP8 - Université Paris 8 Vincennes-Saint-Denis

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Abstract: 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: 2023-07
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Published in Journal of Forecasting, 2023, 42 (4), pp.741-755. ⟨10.1002/for.2916⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-04250346

DOI: 10.1002/for.2916

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