Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks

doi:10.1016/j.apenergy.2022.120452

	Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks
	Li, Dan 1,2,3,4; Li, Yijun 4; Wang, Chaoqun 4; Chen, Min 1,3,5; Wu, Qi 1,4
刊名	APPLIED ENERGY
	2023-02-01
卷号	331 页码:20
关键词	Carbon price forecast Granger forecast Real-time decomposition Neural Granger causality Causal temporal convolutional network
ISSN号	0306-2619
DOI	10.1016/j.apenergy.2022.120452
英文摘要	Recently, global attention has been paid to climate change. On this account, the market-based carbon pricing scheme is developed to limit greenhouse gas emissions, where a proper grasp of the pricing mechanism is crucial for alleviating global warming. Accordingly, we propose a novel method to interpret carbon price dynamics, concurrently deriving the precise prediction and causality. Due to the nonlinearity and nonstationarity of carbon prices, we develop a real-time decomposition approach coupling the multiple ensemble patch transform (MEPT) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The MEPT captures the multi-resolution trends of the carbon prices series exactly, and then the ICEEMDAN extracts the fluctuation patterns. Additionally, we collect the numerous potential factors, involving energy sources, energy prices, stock market indices, and economic information. Furthermore, we developed causal temporal convolutional networks (CTCNs) to realize the accurate prediction and the proper causal inference simultaneously. The experimental results on the European Union Allowance (EUA) confirm the effectiveness and necessity of the real-time MEPT-ICEEMDAN decomposition. Moreover, the proposed MEPT-ICEEMDAN-CTCN model exhibits significant superiority in multi-step-ahead and quantile forecast, which realizes the 0.73881%, 1.04461%, and 1.23495% MAPE in one-, five-, and ten-step-ahead forecast respectively and 0.00032 PD0.1Q and the 0.00285 PD0.9 Q in the quantile forecast. Meanwhile, it reveals the nonlinear Granger causality across the various horizons and quantiles for the first time. It is instructive and inspiring for policymakers, carbon-consumed industries, investors, and researchers.
资助项目	National Natural Science Foundation of China[11690014] ; National Natural Science Foundation of China[11731015] ; Hong Kong Research Grants Council[14206117] ; Hong Kong Research Grants Council[11219420] ; Hong Kong Research Grants Council[11200219] ; CityU SRG-Fd fund, Hong Kong[7005300] ; HK Institute of Data Science, Hong Kong ; Inno HK initiative, The Government of the HKSAR, and Laboratory for AI-Powered Financial Technologies
WOS研究方向	Energy & Fuels ; Engineering
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000905458100001
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/60470]
专题	中国科学院数学与系统科学研究院
通讯作者	Chen, Min; Wu, Qi
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Capital Univ Econ & Business, Sch Stat, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 4.City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China 5.Shanxi Univ, Sch Math Sci, Shanxi 030006, Peoples R China
推荐引用方式 GB/T 7714	Li, Dan,Li, Yijun,Wang, Chaoqun,et al. Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks[J]. APPLIED ENERGY,2023,331:20.
APA	Li, Dan,Li, Yijun,Wang, Chaoqun,Chen, Min,&Wu, Qi.(2023).Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks.APPLIED ENERGY,331,20.
MLA	Li, Dan,et al."Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks".APPLIED ENERGY 331(2023):20.