In this study, the novel hybrid machine learning approach is proposed in carbon price fluctuation prediction. Specifically, a research framework integrating DILATED Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural network algorithm is proposed. The advantage of the combined framework is that it can make feature extraction more efficient. Then, based on the DILATED CNN-LSTM framework, the L1 and L2 parameter norm penalty as regularization method is adopted to predict. Referring to the characteristics of high correlation between energy indicator price and blockchain information in previous literature, and we primarily includes indicators related to blockchain information through regularization process. Based on the above methods, this paper uses a dataset containing an amount of data to carry out the carbon price prediction. The experimental results show that the DILATED CNN-LSTM framework is superior to the traditional CNN-LSTM architecture. Blockchain information can effectively predict the price. Since parameter norm penalty as regularization, Ridge Regression (RR) as L2 regularization is better than Smoothly Clipped Absolute Deviation Penalty (SCAD) as L1 regularization in price forecasting. Thus, the proposed RR-DILATED CNN-LSTM approach can effectively and accurately predict the fluctuation trend of the carbon price. Therefore, the new forecasting methods and theoretical ecology proposed in this study provide a new basis for trend prediction and evaluating digital assets policy represented by the carbon price for both the academia and practitioners.

翻译：本研究提出了一种用于碳价格波动预测的新型混合机器学习方法。具体而言，提出了一种整合膨胀卷积神经网络（DILATED CNN）与长短期记忆（LSTM）神经网络算法的研究框架。该组合框架的优势在于能够使特征提取更为高效。随后，基于DILATED CNN-LSTM框架，采用L1与L2参数范数惩罚作为正则化方法进行预测。参考既有文献中能源指标价格与区块链信息高度相关的特征，我们主要通过正则化过程纳入与区块链信息相关的指标。基于上述方法，本文使用包含大量数据的数据集进行碳价格预测。实验结果表明，DILATED CNN-LSTM框架优于传统的CNN-LSTM架构。区块链信息能够有效预测价格。由于采用参数范数惩罚作为正则化手段，在价格预测中，作为L2正则化的岭回归（RR）优于作为L1正则化的平滑削边绝对偏差惩罚（SCAD）。因此，所提出的RR-DILATED CNN-LSTM方法能够有效且准确地预测碳价格的波动趋势。综上，本研究提出的新预测方法与理论体系，为学术界和实践者进行以碳价格为代表的趋势预测及数字资产政策评估提供了新的依据。