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An innovative random forest-based nonlinear ensemble paradigm of improved feature extraction and deep learning for carbon price forecasting

机译：基于创新的随机森林的非线性集成范式，改进的特征提取和深度学习碳价预测

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摘要

Carbon price is the basis of developing a low carbon economy. The accurate carbon price forecast can not only stimulate the actions of enterprises and families, but also encourage the study and development of low carbon technology. However, as the original carbon price series is non-stationary and nonlinear, traditional methods are less robust to predict it. In this study, an innovative nonlinear ensemble paradigm of improved feature extraction and deep learning algorithm is proposed for carbon price forecasting, which includes complete ensemble empirical mode decomposition (CEEMDAN), sample entropy (SE), long short-term memory (LSTM) and random forest (RF). As the core of the proposed model, LSTM enhanced from the recurrent neural network is utilized to establish appropriate prediction models by extracting memory features of the long and short term. Improved feature extraction, as assistant data preprocessing, represents its unique advantage for improving calculating efficiency and accuracy. Removing irrelevant features from original time series through CEEMDAN lets learning easier and it's even better for using SE to recombine similar-complexity modes. Furthermore, compared with simple linear ensemble learning, RF increases the generalization ability for robustness to achieve the final nonlinear output results. Two markets' real data of carbon trading in china are as the experiment cases to test the effectiveness of the above model. The final simulation results indicate that the proposed model performs better than the other four benchmark methods reflected by the smaller statistical errors. Overall, the developed approach provides an effective method for predicting carbon price.
机译：碳价格是发展低碳经济的基础。准确的碳价格预测不仅可以刺激企业和家庭的行为，还可以鼓励低碳技术的研究和发展。然而，由于原始碳价格系列是非静止和非线性的，传统方法无法稳健预测。在本研究中，提出了一种改进特征提取和深度学习算法的创新非线性集合范例，包括碳价格预测，包括完整的集合经验模式分解（CeeMDAN），样本熵（SE），长短短期记忆（LSTM）和随机森林（rf）。作为所提出的模型的核心，利用来自经常性神经网络的LSTM增强，通过提取长期和短期的内存特征来建立适当的预测模型。改进的特征提取作为辅助数据预处理，代表了提高计算效率和准确性的独特优势。通过Ceemdan从原始时间序列中删除无关的功能让我们更轻松地学习，并且使用SE可以更好地重新组合 - 复杂性模式。此外，与简单的线性集合学习相比，RF增加了鲁棒性的泛化能力，以实现最终的非线性输出结果。中国碳交易的两个市场实际数据是测试上述模型的有效性的实验案例。最终仿真结果表明，所提出的模型比较小统计误差反射的其他四个基准方法更好地执行。总的来说，开发方法提供了预测碳价格的有效方法。

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  • 来源
    《Science of the total environment》 |2021年第25期|143099.1-143099.13|共13页
  • 作者

    Jujie Wang; Xin Sun; Qian Cheng; Quan Cui;

  • 作者单位

    School of Management Science and Engineering Nanjing University of Information Science and Technology Nanjing 210044 China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing 210044 China;

    School of Management Science and Engineering Nanjing University of Information Science and Technology Nanjing 210044 China;

    School of Management Science and Engineering Nanjing University of Information Science and Technology Nanjing 210044 China;

    School of Management Science and Engineering Nanjing University of Information Science and Technology Nanjing 210044 China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Carbon price prediction; Hybrid model; Improved feature extraction; Long short-term memory network; Nonlinear ensemble algorithm; Random forest;

    机译：碳价格预测;混合模型;改进的特征提取;长期内存网络长期内存;非线性合奏算法;随机森林;

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