期刊
JOURNAL OF CLEANER PRODUCTION
卷 204, 期 -, 页码 958-964出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2018.09.071
关键词
Carbon spot price forecasting; Hybrid model; Prediction precision; EU ETS
资金
- National Natural Science Foundation of China [71774054, 71761137001, 71403015, 71521002]
- Fundamental Research Funds for the Central Universities [2017MS081]
- Science and Technology Project of State Grid Corporation of China [YDB17201600102]
- key research program of Beijing Social Science Foundation [17JDYJA009]
Carbon spot price forecasting result is important for both policymakers and market participants. However, because of the complex features of carbon spot price, accurate forecasting is very difficult. To achieve a better prediction precision, a hybrid model combined with complete ensemble empirical mode decomposition (CEEMD), co-integration model (CIM), generalized autoregressive conditional heteroskedasticity model (GARCH), and grey neural network (GNN) optimized by ant colony algorithm (ACA) is proposed. Then it is validated by using data collected from European Union emission trading scheme (EU ETS). The results indicate that the performance of the chosen model is remarkably better than that of other models. Therefore, the hybrid model could be used more frequently for carbon spot price forecasting in the future. (C) 2018 Elsevier Ltd. All rights reserved.
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