Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets | |
Wu, Zhaohua1,2; Feng, Jiaxin1,2; Qiao, Fangli3; Tan, Zhe-Min4 | |
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). |
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