Prediction of Length-of-day Using Gaussian Process Regression

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	Prediction of Length-of-day Using Gaussian Process Regression
	Lei, Yu; Guo, Min; Cai, Hongbing; Hu, Dandan; Zhao, Danning
	2015
发表期刊	JOURNAL OF NAVIGATION
ISSN	0373-4633
卷号	68 期号:3 页码:563-575
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.
部门归属	守时理论与方法研究室
收录类别	SCI
语种	英语
文献类型	期刊论文
条目标识符	http://210.72.145.45/handle/361003/7399
专题	守时理论与方法研究室
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.

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