A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China

To cite the provided dataset for building prediction models, please reference the following paper:

Shi H, Wei A, Xu X, Zhu Y, Hu H, Tang S. A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China. J Environ Manage. 2024 Feb 14;352:120131. doi: 10.1016/j.jenvman.2024.120131. Epub 2024 Jan 23. PMID: 38266520.

This dataset encompasses all essential data and code required for constructing prediction models, including CNN, LSTM, CNN-LSTM, CEEMDAN, Boosting, and GRU, as detailed in the referenced article.

若需引用本数据集以构建预测模型，请参照以下论文：
施H、魏A、徐X、朱Y、胡H、唐S. 基于CNN-LSTM的高精度高鲁棒性深度学习模型用于碳价预测——以中国深圳碳市场为例[J]. 环境管理, 2024, 352: 120131. DOI: 10.1016/j.jenvman.2024.120131. 本文于2024年1月23日在线先期发布，2024年2月14日正式刊出。PMID: 38266520.
本数据集涵盖构建预测模型所需的全部核心数据与代码，包含参考文献中详述的卷积神经网络（Convolutional Neural Network, CNN）、长短期记忆网络（Long Short-Term Memory, LSTM）、CNN-LSTM混合模型、完备总体经验模态分解（Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, CEEMDAN）、提升算法（Boosting）以及门控循环单元（Gated Recurrent Unit, GRU）相关实现所需内容。