【摘要】 碳交易价格的有效预测对制定符合国情的碳金融市场政策以及碳金融市场的风险管理都具有重要意义.对此,提出一种基于非结构数据流行学习的碳价格多尺度组合预测方法.首先,利用网络搜索指数提取碳价格相关的非结构化数据,基于等度量映射流行学习对其进行降维;然后,对降维后的非结构化数据、其他影响因素结构化数据、碳交易价格分别进行经验模态分解(Empirical mode decomposition, EMD),得到不同个数的本征模函数(Intrinsic mode function, IMF),并采用Fine-to-coarse方法对IMF进行重构,得到高频序列、低频序列和趋势项;最后,利用自回归积分滑动平均模型(Autoregressive integrated moving average model, ARIMA)、偏最小二乘(Partial least squares, PLS)回归和神经网络对高频数据、低频数据和趋势项进行预测,将3种预测结果进行集成,得到最终预测值.仿真实验结果表明,所提出的方法可以有效利用多源信息,具有较高的预测精度和良好的适用性.更多还原

【Abstract】 Forecasting carbon trading price effectively is of great significance for the formulation of carbon financial market policies that suit to Chinese condition and the risk management of carbon financial market. This paper proposes a multi-scale combined forecast method for carbon price based on manifold learning of unstructured data. Firstly, the unstructured data related to carbon price is extracted using the network search index, and dimensionality reduction is performed based on the isometric mapping manifold learning. Then, the structured data of other influencing factors and the carbon trading price are decomposed into a variable number of intrinsic mode functions(IMFs) using empirical mode decomposition(EMD) respectively. The IMF is reconstructed to get high frequency sequence, low frequency sequence and trend item based on the fine-to-coarse method. Moreover, autoregressive integrated moving average model(ARIMA),Partial least squares(PLS) regression and neural network are used to forecast high-frequency data, low-frequency data and trend items, which are aggregated to get the final forecast result. The results of simulated experiments show that the proposed method can effectively use multi-source information and has high prediction accuracy and good applicability.更多还原