Optimal weight random forest ensemble with Fuzzy C-means cluster-based subsampling for carbon price forecasting

Accurate prediction of carbon price is of great value for production, operation, investment decisions and the establishment of carbon pricing mechanism. However, the large amount of data often limits the application of learning model with good predictive performance in carbon price prediction. There...

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Bibliographic Details
Published inJournal of intelligent & fuzzy systems Vol. 46; no. 1; pp. 991 - 1003
Main Authors Zhang, Yuhua, Li, Yuerong, Che, Jinxing
Format Journal Article
LanguageEnglish
Published Amsterdam IOS Press BV 10.01.2024
Subjects
Algorithms
Carbon
Clusters
Complexity
Decision trees
Emission analysis
Emissions
Forecasting
Machine learning
Mathematical models
Performance prediction
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Summary:Accurate prediction of carbon price is of great value for production, operation, investment decisions and the establishment of carbon pricing mechanism. However, the large amount of data often limits the application of learning model with good predictive performance in carbon price prediction. Therefore, the development of learning algorithms with low computational complexity has become a research hotspot. Among them, subsampling integration technology is an effective method to reduce the computational complexity. However, lack of data representativeness in subsamples and ignorance of differences among submodels inhibit the prediction performance of the subsampled ensemble model. This project proposes an optimal weight random forest ensemble model with cluster-based subsampling (FCM-OWSRFE) for carbon price forecasting. Firstly, Fuzzy C-means cluster-based subsampling to ensure the data representativeness of subsamples. Secondly, a series of sub-random forest models are built based on subsamples with data representativeness. Finally, an optimal weight ensemble model from these sub-models is derived. To verify the validity of the model, we test FCM-OWSRFE model with the carbon price of Guangzhou Emission Exchange and the carbon price of Hubei Carbon Emission Exchange, respectively. Experimental results show that Fuzzy C-means cluster-based subsampling and the optimal weight scheme can efficiently improve the prediction performance of the subsampled random forest ensemble model.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-233422