基于滚动时间窗的碳市场价格分解集成预测研究

doi:10.16381/j.cnki.issn1003-207x.2022.0122

Abstract

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.

Key words: carbon market price; decomposition ensemble prediction; singular spectrum analysis; sliding time window

CLC Number:

F206
F832.5

FAN Li-wei, DONG Huan-huan, JIAN Ling. A Decomposition Ensemble Model with Sliding Time Window for Forecasting Carbon Market Prices[J]. Chinese Journal of Management Science, 2023, 31(1): 277-286.

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

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