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• W4200304831 abstract "In the current context of pursuing carbon neutrality and carbon peaking, many countries are accelerating the construction of carbon trading markets. Accurate prediction of carbon prices can enable national carbon trading markets to play a role in carbon emission reduction as soon as possible. However, current research is limited mostly to point forecasting of carbon prices, which makes it difficult to guarantee the stability of forecasting results in an increasingly complex market. Therefore, this paper proposes a three-stage vertical carbon price interval prediction framework. The contributions of this paper are as follows: the selection process of the decomposition model is regarded as an important process of prediction; a backpropagation neural network optimized by the sparrow search algorithm (SSA-BPNN) is used for the point prediction of carbon prices as a first attempt; and the kernel density estimation (KDE) model is used for interval estimation based on the point prediction results, which improves the confidence of the prediction. To validate the framework, this paper uses Shenzhen SZA-2014 products as the sample. The results show that the root mean square error of the predicted result with the improved complete ensemble empirical mode decomposition model (ICEEMD) is reduced by 29.7% and the use of SSA increases the predicted R2 by 8.5% compared with other optimization algorithms. In addition, the prediction interval coverage probability of interval prediction reaches 86% under 70% confidence. These results show that the proposed framework is not only more effective in point prediction but also performs well in interval prediction." @default.
• W4200304831 created "2021-12-31" @default.
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• W4200304831 date "2022-02-01" @default.
• W4200304831 modified "2023-10-11" @default.
• W4200304831 title "A three-stage framework for vertical carbon price interval forecast based on decomposition–integration method" @default.
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• W4200304831 doi "https://doi.org/10.1016/j.asoc.2021.108204" @default.
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