A secondary-decomposition-ensemble learning paradigm for forecasting PM2.5 concentration

doi:10.1016/j.apr.2018.03.008

CSpace > 系统科学研究所

	A secondary-decomposition-ensemble learning paradigm for forecasting PM2.5 concentration
	Gan, Kai 1; Sun, Shaolong 2,3,4; Wang, Shouyang2,3,6 ; Wei, Yunjie 2,5,6
	2018-11-01
发表期刊	ATMOSPHERIC POLLUTION RESEARCH
ISSN	1309-1042
卷号	9 期号:6 页码:989-999
摘要	To design high-accuracy tools for hourly PM2.5 concentration forecasting, we propose a new method based on the secondary-decomposition-ensemble learning paradigm. Prior to forecasting, the original PM2.5 concentration series are processed using secondary-decomposition (SD): (1) wavelet packet decomposition (WPD) is used to decompose the time series into low-frequency components and high-frequency components; (2) the high-frequency components are further decomposed by the complementary ensemble empirical mode decomposition (CEEMD) algorithm. Then Phase space reconstruction (PSR) is utilized to determine the optimal input form of each intrinsic mode function (IMF). The least square support vector regression (LSSVR) model, optimized by the chaotic particle swarm optimization method combined with the gravitation search algorithm (CPSOGSA), is employed to model all reconstructed components independently. Finally, the predict results of these components are integrated into an aggregated output as the final prediction, utilizing another LSSVR optimized by CPSOGSA as an ensemble forecasting tool. Our empirical results show that this method outperforms the benchmark methods in both level and directional forecasting accuracy.
关键词	Secondary-decomposition-ensemble learning paradigm Complementary ensemble empirical mode decomposition Phase space reconstruction Least square support vector regression Hybrid intelligent algorithm
DOI	10.1016/j.apr.2018.03.008
语种	英语
资助项目	National Natural Science Foundation of China[71501176] ; China Postdoctoral Science Foundation[2015M580141]
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:000447035900002
出版者	TURKISH NATL COMMITTEE AIR POLLUTION RES & CONTROL-TUNCAP
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/31337
专题	系统科学研究所
通讯作者	Sun, Shaolong
作者单位	1.Lanzhou Univ, Sch Math & Stat, Lanzhou 730000, Gansu, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China 4.City Univ Hong Kong, Dept Syst Engn & Engn Management, Tat Chee Ave, Kowloon, Hong Kong, Peoples R China 5.City Univ Hong Kong, Dept Management Sci, Tat Chee Ave, Kowloon, Hong Kong, Peoples R China 6.Chinese Acad Sci, Ctr Forecasting Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Gan, Kai,Sun, Shaolong,Wang, Shouyang,et al. A secondary-decomposition-ensemble learning paradigm for forecasting PM2.5 concentration[J]. ATMOSPHERIC POLLUTION RESEARCH,2018,9(6):989-999.
APA	Gan, Kai,Sun, Shaolong,Wang, Shouyang,&Wei, Yunjie.(2018).A secondary-decomposition-ensemble learning paradigm for forecasting PM2.5 concentration.ATMOSPHERIC POLLUTION RESEARCH,9(6),989-999.
MLA	Gan, Kai,et al."A secondary-decomposition-ensemble learning paradigm for forecasting PM2.5 concentration".ATMOSPHERIC POLLUTION RESEARCH 9.6(2018):989-999.