Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets

doi:10.1098/rsta.2015.0197

	Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets
	Wu, Zhaohua 1,2; Feng, Jiaxin 1,2; Qiao, Fangli 3; Tan, Zhe-Min 4
刊名	PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
	2016-04-13
卷号	374 期号:2065
关键词	data compression fast algorithm multidimensional ensemble empirical mode decomposition principal component analysis empirical orthogonal function adaptive and local data analysis
ISSN号	1364-503X
DOI	10.1098/rsta.2015.0197
英文摘要	In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders.
资助项目	NSFC[41461164008] ; NSFC[41130964]
WOS关键词	NONSTATIONARY TIME-SERIES ; IMAGE COMPRESSION ; ANNUAL CYCLE ; ORTHOGONAL FUNCTIONS ; HILBERT SPECTRUM ; PART I ; OSCILLATION ; VARIABILITY ; PATTERNS ; TRENDS
WOS研究方向	Science & Technology - Other Topics
语种	英语
出版者	ROYAL SOC
WOS记录号	WOS:000372553500005
内容类型	期刊论文
源URL	[http://ir.fio.com.cn:8080/handle/2SI8HI0U/25561]
专题	自然资源部第一海洋研究所
通讯作者	Wu, Zhaohua
作者单位	1.Florida State Univ, Dept Earth Ocean & Atmospher Sci, Tallahassee, FL 32306 USA 2.Florida State Univ, Ctr Ocean Atmospher Predict Studies, Tallahassee, FL 32306 USA 3.SOA, Inst Oceanog 1, Qingdao, Shangdong Provi, Peoples R China 4.Nanjing Univ, Sch Atmospher Sci, Nanjing 210008, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Wu, Zhaohua,Feng, Jiaxin,Qiao, Fangli,et al. Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets[J]. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES,2016,374(2065).
APA	Wu, Zhaohua,Feng, Jiaxin,Qiao, Fangli,&Tan, Zhe-Min.(2016).Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets.PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES,374(2065).
MLA	Wu, Zhaohua,et al."Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets".PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES 374.2065(2016).