
碳交易价格预测对政策制定与市场稳态的维护至关重要,但碳价时间序列的非线性、非平稳性等特征给其精准预测带来困难。为此,提出基于时序特征向量映射模块、双向门控循环单元和自注意力机制融合的深度学习模型。模型集成3个分支:直接处理原始碳价时间序列;构建碳价序列多尺度分量矩阵;基于灰色关联度分析与极端随机树方法筛选出与碳价相关的关键变量。各分支均利用时序特征向量映射模块编码时间信息。双向门控循环单元捕捉双向长时依赖。自注意力机制模型加权特征进行综合集成。实证研究显示,该模型单步及多步预测精度高,均优于基准模型,模型具有优越性与稳健性。
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0000 0001 1956 3178, grid.434213.3, Institute for Sustainable Development and International Relations, Paris, France,0000 0004 1936 7494, grid.61971.38, School of Resource and Environmental Management, Faculty of the Environment, Simon Fraser University, Burnaby, British Columbia, Canada,0000 0004 0641 4845, grid.424447.5, Centre International de Recherche sur l’Environnement et le Développement (CIRED), Ecole des Ponts ParisTech, Nogent-sur-Marne, France,0000 0004 0482 9086, grid.431778.e, Climate Change Group, World Bank, Washington, DC, USA,0000 0001 2113 8111, grid.7445.2, Grantham Institute, Imperial College London, London, UK,0000 0001 1955 9478, grid.75276.31, Energy Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria,0000 0001 2156 2780, grid.5801.c, Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland,0000 0004 1936 8948, grid.4991.5, Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK,0000 0001 1956 3178, grid.434213.3, Institute for Sustainable Development and International Relations, Paris, France.Carbon prices across countries[J].Nature Climate Change,2018.
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