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论文

基于滚动时间窗的碳市场价格分解集成预测研究

  • 范丽伟 ,
  • 董欢欢 ,
  • 渐令
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  • 中国石油大学华东经济管理学院,山东 青岛266580

收稿日期: 2022-01-17

  修回日期: 2022-05-01

  网络出版日期: 2023-02-09

基金资助

国家重点研发计划资助项目(2021YFA1000102);国家自然科学基金资助项目(71934007)

A Decomposition Ensemble Model with Sliding Time Window for Forecasting Carbon Market Prices

  • FAN Li-wei ,
  • DONG Huan-huan ,
  • JIAN Ling
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  • School of Economics and Management, China University of Petroleum, Qingdao 266580, China

Received date: 2022-01-17

  Revised date: 2022-05-01

  Online published: 2023-02-09

摘要

提高碳市场价格预测准确性对于交易风险监测以及碳市场平稳发展具有重要价值。针对复杂的、非线性碳市场价格数据的短期预测误差偏大、分解过程易产生数据泄露问题,提出了基于滚动时间窗的SSA-SVR分解集成预测框架。首先,选取时间窗数据,继而借助奇异谱分析将时间窗内碳价序列分解重构为高、低频序列;然后,使用支持向量回归方法对高、低频序列分别进行预测;最后,加和集成预测结果,得到下一时刻的碳市场价格预测值。通过不断更新时间窗的数据内容,动态执行“分解-预测-集成”过程,实现碳市场价格的实时预测。研究结果表明,本文所提出框架表现出优异且稳定的预测性能,在碳市场价格预测研究中具有良好的适用性和有效性。

本文引用格式

范丽伟 , 董欢欢 , 渐令 . 基于滚动时间窗的碳市场价格分解集成预测研究[J]. 中国管理科学, 2023 , 31(1) : 277 -286 . DOI: 10.16381/j.cnki.issn1003-207x.2022.0122

Abstract

Improving the accuracy of carbon market price forecasting is of significance for the monitoring of trading risk and the stable development of the carbon market. Aiming at the problems of large errors in the short-term forecasting of complex and nonlinear carbon market prices and data leakage in the decomposition process, a SSA-SVR decomposition ensemble prediction framework with sliding time window is proposed. Firstly, the time window data are selected, decomposed and reconstructed into high and low frequency sequences by using singular spectrum analysis and singular entropy. Then, support vector regression algorithm is used to forecast the high and low frequency sequences. Finally, the one step ahead of carbon market price forecasting value is obtained by adding and integrating the above results. By continuously updating the data content of the time window and dynamically executing the process of “decomposition-forecasting-integration”, real-time forecasting of carbon market price is realized. The empirical results show that the forecasting framework proposed in this paper exhibits satisfactory and stable forecasting performance, which is a suitable and effective tool for forecasting carbon market prices.

参考文献

[1] Zhang Yuejun, Wei Yiming. An overview of current research on EU ETS: evidence from its operating mechanism and economic effect[J]. Applied Energy, 2010, 87(6): 1804-1814.
[2] 张跃军, 魏一鸣. 国际碳期货价格的均值回归: 基于EU ETS的实证分析[J]. 系统工程理论与实践, 2011, 31(2): 214-220.Zhang Yuejun, Wei Yiming. Interpreting the mean reversion of international carbon futures price: empirical evidence from the EU ETS[J]. Systems Engineering—Theory& Practice, 2011, 31(2): 214-220.
[3] Zhu Bangzhu, Shi Xuetao, Chevallier J. An adaptive multiscale ensemble learning paradigm for nonstationary and nonlinear energy price time series forecasting[J]. Journal of Forecasting, 2016, 35(7): 633-651.
[4] Byun S J, Cho H. Forecasting carbon futures volatility using GARCH models with energy volatilities[J]. Energy Economics, 2013, 40: 207-221.
[5] 王娜. 基于Boosting-ARMA的碳价预测[J]. 统计与信息论坛, 2017, 32(3): 28-34.Wang Na. Forecasting of carbon price based on Boosting-ARMA model[J]. Statistics & Information Forum, 2017(3): 28-34.
[6] Chevallier J. Nonparametric modeling of carbon prices[J]. Energy Economics, 2011, 33(6): 1267-1282.
[7] 朱帮助, 魏一鸣. 基于GMDH-PSO-LSSVM的国际碳市场价格预测[J]. 系统工程理论与实践, 2011, 31(12): 2264-2271.Zhu Bangzhu, Wei Yiming. Carbon price prediction based on integration of GMDH, particle swarm optimization and least squares support vector machines[J]. Systems Engineering—Theory& Practice, 2011, 31(12): 2264-2271.
[8] Fan Xinghua, Li Shasha, Tian Lixin. Chaotic characteristic identification for carbon price and an multi-layer perceptron network prediction model[J]. Expert Systems with Applications, 2015, 42(8): 3945-3952.
[9] Atsalakis G S. Using computational intelligence to forecast carbon prices[J]. Applied Soft Computing, 2016, 43: 107-116.
[10] Zhu Bangzhu. A novel multiscale ensemble carbon price prediction model integrating empirical mode decomposition, genetic algorithm and artificial neural network[J]. Energies, 2012, 5(2): 355-370.
[11] 高杨, 李健. 基于EMD-PSO-SVM误差校正模型的国际碳金融市场价格预测[J]. 中国人口·资源与环境, 2014, 24(6): 163-170.Gao Yang, Li Jian. International carbon finance market price prediction based on EMD-PSO-SVM error correction model[J]. China Population, Resources and Environment, 2014, 24(6): 163-170.
[12] Zhou Jianguo, Huo Xuejing, Xu Xiaolei, et al. Forecasting the carbon price using extreme-point symmetric mode decomposition and extreme learning machine optimized by the grey wolf optimizer algorithm[J]. Energies, 2019, 12(5): 950.
[13] Sun Wei, Duan Ming. Analysis and forecasting of the carbon price in China’s regional carbon markets based on fast ensemble empirical mode decomposition, phase space reconstruction, and an improved extreme learning machine[J]. Energies, 2019, 12(2): 277-303.
[14] Zhou Feite, Huang Zhehao, Zhang Changhong. Carbon price forecasting based on CEEMDAN and LSTM[J]. Applied Energy, 2022, 311: 118601.
[15] Zhu Bangzhu, Han Dong, Wang Ping, et al. Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression[J]. Applied Energy, 2017, 191: 521-530.
[16] Lu Hongfang, Ma Xin, Huang Kun, et al. Carbon trading volume and price forecasting in China using multiple machine learning models[J]. Journal of Cleaner Production, 2020, 249: 119386.
[17] 崔金鑫, 邹辉文. 基于CEEMDAN-MR-PE-NLE多频优化组合模型的碳金融市场价格预测[J]. 数学的实践与认识, 2020, 50(3): 105-120.Cui Jinxin, Zou Huiwen. Carbon financial market price forecasting based on CEEMDAN-MR-PE-NLE multi-frequency optimization combined model[J]. Journal of Mathematics in Practice and Theory, 2020, 50(3): 105-120.
[18] Sun Wei, Xu Chang. Carbon price prediction based on modified wavelet least square support vector machine[J]. Science of the Total Environment, 2021, 754: 142052.
[19] Zhu Bangzhu, Ye Shunxin, Wang Ping, et al. A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting[J]. Energy Economics, 2018, 70: 143-157.
[20] 张晨, 杨仙子. 基于多频组合模型的中国区域碳市场价格预测[J]. 系统工程理论与实践, 2016, 36(12): 3017-3025.Zhang Chen, Yang Xianzi. Forecasting of China’s regional carbon market price based on multi-frequency combined model[J]. Systems Engineering—Theory& Practice, 2016, 36(12): 3017-3025.
[21] Zhang Jinliang, Li Dezhi, Hao Yu, et al. A hybrid model using signal processing technology, econometric models and neural network for carbon spot price forecasting[J]. Journal of Cleaner Production, 2018, 204: 958-964.
[22] Huang Yumeng, Dai Xingyu, Wang Qunwei, et al. A hybrid model for carbon price forecasting using GARCH and long short-term memory network[J]. Applied Energy, 2021, 285: 116485.
[23] Sun Wei, Huang Chenchen. A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network[J]. Journal of Cleaner Production, 2020, 243: 118671.
[24] Qian Zheng, Pei Yan, Zareipour H, et al. A review and discussion of decomposition-based hybrid models for wind energy forecasting applications[J]. Applied Energy, 2019, 235: 939-953.
[25] Du Kongchang, Zhao Ying, Lei Jiaqiang. The incorrect usage of singular spectral analysis and discrete wavelet transform in hybrid models to predict hydrological time series[J]. Journal of Hydrology, 2017, 552: 44-51.
[26] Wang Yamin, Wu Lei. On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation[J]. Energy, 2016, 112: 208-220.
[27] Shao Zhen, Han Jun, Zhao Wei, et al. Hybrid model for short-term wind power forecasting based on singular spectrum analysis and a temporal convolutional attention network with an adaptive receptive field[J]. Energy Conversion and Management, 2022, 269: 116138.
[28] 梁小珍, 乔晗, 汪寿阳, 等. 基于奇异谱分析的我国航空客运量集成预测模型[J]. 系统工程理论与实践, 2017, 37(6): 1479-1488.Liang Xiaozhen, Qiao Han, Wang Shouyang, et al. An integrated forecasting model for air passenger traffic in China based on singular spectrum analysis[J]. Systems Engineering—Theory& Practice, 2017, 37(6): 1479-1488.
[29] 梁小珍, 郭战坤, 张倩文, 等. 基于奇异谱分析的航空客运需求分析与分解集成预测模型[J]. 系统工程理论与实践, 2020, 40(7): 1844-1855.Liang Xiaozhen, Guo Zhankun, Zhang Qianwen, et al. An analysis and decomposition ensemble prediction model for air passenger demand based on singular spectrum analysis[J]. Systems Engineering—Theory& Practice, 2020, 40(7): 1844-1855.
[30] 王珏, 齐琛, 李明芳. 基于SSA-ELM的大宗商品价格预测研究[J]. 系统工程理论与实践, 2017, 37(8): 2004-2014.Wang Jue, Qi Chen, Li Mingfang. Prediction of commodity prices based on SSA-ELM[J]. Systems Engineering—Theory& Practice, 2017, 37(8): 2004-2014.
[31] Zhang Xiaobo, Wang Jianzhou, Gao Yuyang. A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and SVM[J]. Energy Economics, 2019,81: 899-913.
[32] Wang Cong, Zhang Hongli, Ma Ping. Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network [J]. Applied Energy, 2020, 259: 114139.
[33] 刘金培, 罗瑞, 陈华友, 等. 非结构化数据驱动的混合二次分解汇率区间多尺度组合预测[J/OL]. 中国管理科学. https://doi.org/10.16381/j.cnki.issn1003-207x.2021.0194.Liu Jinpei, Luo Rui, Chen Huayou, et al. Unstructured data-driven multi-scale exchange rate interval combined forecasting based on hybrid secondary decomposition[J/OL]. Chinese Journal of Management Science. https://doi.org/10.16381/j.cnki.issn1003-207x.2021.0194.
[34] Colebrook J M, Reid P C, Coombs S H. Continuous plankton records: a change in the plankton of the southern North Sea between 1970 and 1972[J]. Marine Biology, 1978, 45(3): 209-213.
[35] 杨文献, 任兴民, 姜节胜. 基于奇异熵的信号降噪技术研究[J]. 西北工业大学学报, 2001, 19(3): 368-371.Yang Wenxian, Ren Xingmin, Jiang Jiesheng. On improving the effectiveness of the new noise reduction technique based on singularity spectrum[J]. Journal of Northwestern Polytechnical University, 2001, 19(3): 368-371.
[36] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273-297.
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