准确预测碳价格不仅有助于投资者及监管部门的科学决策，而且有助于碳金融市场的健康发展。考虑碳价格预测的复杂性，基于“分解－重构－预测－集成”的建模原则，构建了多尺度碳价格集成组合预测模型。首先，采用改进型自适应白噪声完备集成经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise，ICEEMDAN)算法对碳价原始序列进行分解，并以综合贡献度指数(comprehensive contribution index，CCI)对分量进行重构，得到短期、长期和趋势分量；然后，采用门限广义自回归条件异方差(threshold generalized auto-regressive conditional heteroscedasticity，TGARCH)模型预测短期分量，以布谷鸟搜索(cuckoo search，CS)算法优化超参数的长短期记忆(long-short term memory，LSTM)神经网络预测长期和趋势分量；在此基础上，采用非线性集成算法对各分量预测结果进行集成，得到最终的碳价预测结果。以湖北碳市场为样本数据进行实证分析，结果表明所构建的预测模型性能最优，预测结果更准确，可为监管部门和企业决策提供有效信息。

Precise prediction of carbon prices is not only of significance for policy formulation and investment decisions, but also helpful to the carbon finance market development. Considering the non-stationary and nonlinearity characteristics inherent in the carbon price, this study proposed a novel hybrid model named ICEEMDAN-TGARCH/LSTM(CS)-LSTM(CS), in which the ICEEMDAN (improved complete ensemble empirical mode decomposition with adaptive noise) is applied to decompose the carbon price original series into several subcomponents, then the subcomponents are identified according to comprehensive contribution index (CCI) and divided into short-term, long-term and trend components. TGARCH(threshold generalized auto-regressive conditional heteroscedasticity) is chosen for the short-term components forecasting, while LSTM (long-short term memory) neural network model with hyper-parameters optimized by cuckoo search (CS) algorithm is selected to forecast other components and combine all the forecasting sequences. The empirical results Hubei carbon emission trading market indicated that the proposed model outperformed other benchmark models with the lowest prediction error, meaning that the hybrid model proposed by us can be an effective and accurate tool for carbon price forecasting. It provides effective information for regulatory authorities and enterprises to make decisions.