Middle and long-term prediction of UT1-UTC based on combination of Gray Model and Autoregressive Integrated Moving Average

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• 作者：Song Jia^a ; Tian-he Xu^b ; ^c ; ^{thxugie@163.com} ; Zhang-zhen Sun^b ; Jia-jing Li^a
• 关键词：UT1-UTC ; Prediction ; GM(1 ;   ; 1) ; ARIMA
• 刊名：Advances in Space Research
• 出版年：2017
• 出版时间：1 February 2017
• 年：2017
• 卷：59
• 期：3
• 页码：888-894
• 全文大小：497 K
• 卷排序：59

文摘

UT1-UTC is an important part of the Earth Orientation Parameters (EOP). The high-precision predictions of UT1-UTC play a key role in practical applications of deep space exploration, spacecraft tracking and satellite navigation and positioning. In this paper, a new prediction method with combination of Gray Model (GM(1, 1)) and Autoregressive Integrated Moving Average (ARIMA) is developed. The main idea is as following. Firstly, the UT1-UTC data are preprocessed by removing the leap second and Earth’s zonal harmonic tidal to get UT1R-TAI data. Periodic terms are estimated and removed by the least square to get UT2R-TAI. Then the linear terms of UT2R-TAI data are modeled by the GM(1, 1), and the residual terms are modeled by the ARIMA. Finally, the UT2R-TAI prediction can be performed based on the combined model of GM(1, 1) and ARIMA, and the UT1-UTC predictions are obtained by adding the corresponding periodic terms, leap second correction and the Earth’s zonal harmonic tidal correction. The results show that the proposed model can be used to predict UT1-UTC effectively with higher middle and long-term (from 32 to 360 days) accuracy than those of LS + AR, LS + MAR and WLS + MAR.