| 基于EMD-GA-BP与EMD-PSO-LSSVM的中国碳市场价格预测 |
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| 引用本文： | 崔焕影,窦祥胜.基于EMD-GA-BP与EMD-PSO-LSSVM的中国碳市场价格预测[J].运筹与管理,2018,27(7):133-143. |
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| 作者姓名： | 崔焕影 窦祥胜 |
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| 作者单位： | 西南交通大学 经济管理学院,四川 成都 610031 |
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| 基金项目： | 国家社会科学基金项目“西部地区低碳经济发展道路、模式与机制研究”(10XJY004) |
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| 摘 要： | 由于碳交易市场价格的波动性大及相互影响关系的复杂性,本文试图构建碳价格长期和短期的最优预测模型。考虑到碳交易价格波动的趋势性和周期性特点,基于经验模态分解算法(EMD)、遗传算法(GA)—神经网络(BP)模型、粒子群算法(PSO)—最小二乘支持向量机(LSSVM)模型及由它们构建的组合预测模型,对中国碳市场交易价格进行短期预测和长期预测。实证分析中将影响碳交易价格的不同宏观经济因素和碳价格时间序列因素做为输入变量,分别代入组合模型进行预测。研究结果表明,在短期预测中,EMD-GA-BP模型预测效果优于GA-BP模型和PSO-LSSVM模型;而在长期预测中,组合模型EMD-PSO-LSSVM模型预测效果优于只考虑碳价格波动趋势性或周期性预测效果。
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| 关 键 词： | 碳价格预测 经验模态分解算法 遗传算法—神经网络 粒子群算法-最小二乘支持向量机 宏观经济因素 |
| 收稿时间： | 2017-03-18 |
Carbon Price Forecasts in Chinese Carbon Trading Market Based on EMD-GA-BP and EMD-PSO-LSSVM |
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| CUI Huan-ying,DOU Xiang-sheng.Carbon Price Forecasts in Chinese Carbon Trading Market Based on EMD-GA-BP and EMD-PSO-LSSVM[J].Operations Research and Management Science,2018,27(7):133-143. |
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| Authors: | CUI Huan-ying DOU Xiang-sheng |
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| Affiliation: | School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China |
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| Abstract: | Because of large price fluctuation in carbon trading market and its complicated influence on carbon price caused by some uncertainty factors, the short-term and the long-term optimal forecasting models are built to forecast the carbon price. Considering the periodicity and?trend of carbon price fluctuation, this paper conducts the short-and the long-term forecasts of China's carbon price based on empirical mode decomposition (EMD), genetic algorithm and back propagation neural net model (GA-BP), particle swarm optimization and least squares support vector machines model(PSO-LSSVM) and their combination models. Different macroeconomic factors affecting carbon price and carbon price time series are respectively employed as input variables into the combination models and two sub-models for forecasting in empirical analysis. The results show that, the EMD-GA-BP model performs better than the GA-BP model and the PSO-LSSVM model in the short-term forecasting, while the combination models (EMD-PSO-LSSVM) perform better than the sub-models in the long-term forecasting. |
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| Keywords: | carbon price forecasting empirical mode decomposition genetic algorithm and back propagation neural net particle swarm optimization and least squares support vector machines macroeconomic factors |
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