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

文献类型：期刊论文

作者Gan, Kai1; Sun, Shaolong2,3,4; Wang, Shouyang2,3,6; Wei, Yunjie2,5,6
刊名ATMOSPHERIC POLLUTION RESEARCH
出版日期2018-11-01
卷号9期号:6页码:989-999
关键词Secondary-decomposition-ensemble learning paradigm Complementary ensemble empirical mode decomposition Phase space reconstruction Least square support vector regression Hybrid intelligent algorithm
ISSN号1309-1042
DOI10.1016/j.apr.2018.03.008
英文摘要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.
资助项目National Natural Science Foundation of China[71501176] ; China Postdoctoral Science Foundation[2015M580141]
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000447035900002
出版者TURKISH NATL COMMITTEE AIR POLLUTION RES & CONTROL-TUNCAP
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/31336]  
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.

入库方式： OAI收割

来源：数学与系统科学研究院

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