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A secondary-decomposition-ensemble learning paradigm for forecasting PM2.5 concentration
Gan, Kai1; Sun, Shaolong2,3,4; Wang, Shouyang2,3,6; Wei, Yunjie2,5,6
2018-11-01
Source PublicationATMOSPHERIC POLLUTION RESEARCH
ISSN1309-1042
Volume9Issue:6Pages:989-999
AbstractTo 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.
KeywordSecondary-decomposition-ensemble learning paradigm Complementary ensemble empirical mode decomposition Phase space reconstruction Least square support vector regression Hybrid intelligent algorithm
DOI10.1016/j.apr.2018.03.008
Language英语
Funding ProjectNational Natural Science Foundation of China[71501176] ; China Postdoctoral Science Foundation[2015M580141]
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000447035900002
PublisherTURKISH NATL COMMITTEE AIR POLLUTION RES & CONTROL-TUNCAP
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/31337
Collection系统科学研究所
Affiliation1.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
Recommended Citation
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.
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