Prediction of Length-of-day Using Gaussian Process Regression

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	Prediction of Length-of-day Using Gaussian Process Regression
	Lei, Yu 1,2; Guo, Min 3; Cai, Hongbing 1; Hu, Dandan3 ; Zhao, Danning 1,2
刊名	journal of navigation
	2015-05-01
卷号	68 期号:3 页码:563-575
关键词	Length-Of-Day LOD) Prediction Gaussian Process Regression (GPR)
英文摘要	the predictions of length-of-day (lod) are studied by means of gaussian process regression (gpr). the eop c04 time-series with daily values from the international earth rotation and reference systems service (iers) serve as the data basis. firstly, well known effects that can be described by functional models, for example effects of the solid earth and ocean tides or seasonal atmospheric variations, are removed a priori from the c04 time-series. only the differences between the modelled and actual lod, i.e. the irregular and quasi-periodic variations, are employed for training and prediction. different input patterns are discussed and compared so as to optimise the gpr model. the optimal patterns have been found in terms of the prediction accuracy and efficiency, which conduct the multi-step ahead predictions utilising the formerly predicted values as inputs. finally, the results of the predictions are analysed and compared with those obtained by other prediction methods. it is shown that the accuracy of the predictions are comparable with that of other prediction methods. the developed method is easy to use.
WOS标题词	science & technology ; technology ; physical sciences
类目[WOS]	engineering, marine ; oceanography
研究领域[WOS]	engineering ; oceanography
关键词[WOS]	least-squares ; earth ; parameters
收录类别	SCI
语种	英语
WOS记录号	WOS:000352011100010
公开日期	2015-07-28
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/24125]
专题	西安光学精密机械研究所_研究生部
作者单位	1.Chinese Acad Sci, Natl Time Serv Ctr, Beijing 100864, Peoples R China 2.Univ Chinese Acad Sci, Jinan, Peoples R China 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Beijing 100864, Peoples R China
推荐引用方式 GB/T 7714	Lei, Yu,Guo, Min,Cai, Hongbing,et al. Prediction of Length-of-day Using Gaussian Process Regression[J]. journal of navigation,2015,68(3):563-575.
APA	Lei, Yu,Guo, Min,Cai, Hongbing,Hu, Dandan,&Zhao, Danning.(2015).Prediction of Length-of-day Using Gaussian Process Regression.journal of navigation,68(3),563-575.
MLA	Lei, Yu,et al."Prediction of Length-of-day Using Gaussian Process Regression".journal of navigation 68.3(2015):563-575.