摘要
最小二乘(Least Squares,LS)与自回归(Auto Regressive,AR)联合(LS+AR)模型在极移预报(polar motion,PM)中存在以下问题:最小二乘拟合的内部残差值较好而LS外推的残差值较大;LS拟合残差序列是非线性的,故根据预报历元前的残差序列建立的AR模型可能并不适用于待预报的残差序列,存在不匹配预报的情况.针对这两个问题,通过以下方法进行解决:首先对LS拟合数据两端点附加约束条件使其固定到LS拟合曲线上,因此在两端点附近的拟合值与观测值十分接近;然后选取与LS外推残差序列变化趋势接近的内推残差序列作为AR模型的建模对象,进行残差预报.通过实例表明该方法能够有效地提高LS+AR模型在短期极移预报的精度.此外,通过与RLS(Robustified Least Squares)+AR、RLS+ARIMA(Auto Regressive Integrated Moving Average)和LS+ANN(Artificial Neural Network)模型的预报结果对比,证明了该方法在极移预报中的可行性.实例证明了所提出的方法在短期预报中可以取得良好的预报结果,尤其在1–10d超短期的极移预报上可以获得与国际最好预报精度相当的预报结果.
There are two problems of the LS （Least Squares）＋AR （AutoRegressive） model in polar motion forecast： the inner residual value of LS fitting is reasonable, but the residual value of LS extrapolation is poor; and the LS fitting residual sequence is non-linear. It is unsuitable to establish an AR model for the residual sequence to be forecasted, based on the residual sequence before forecast epoch. In this paper, we make solution to those two problems with two steps. First, restrictions are added to the two endpoints of LS fitting data to fix them on the LS fitting curve. Therefore, the fitting values next to the two endpoints are very close to the observation values. Secondly,we select the interpolation residual sequence of an inward LS fitting curve, which has a similar variation trend as the LS extrapolation residual sequence, as the modeling object of AR for the residual forecast. Calculation examples show that this solution can effectively improve the short-term polar motion prediction accuracy by the LS＋AR model. In addition, the comparison results of the forecast models of RLS （Robustified Least Squares）＋AR, RLS＋ARIMA （AutoRegressive Integrated Moving Average）, and LS＋ANN （Artificial Neural Network） confirm the feasibility and effectiveness of the solution for the polar motion forecast. The results, especially for the polar motion forecast in the 1-10 days, show that the forecast accuracy of the proposed model can reach the world level.
出处
《天文学报》
CSCD
北大核心
2017年第2期65-75,共11页
Acta Astronomica Sinica
基金
国家自然科学基金项目(41404033)
国家科技基础性工作专项(015FY310200)
国家重点实验室开放基金重点项目(SKLGIE2014-Z-1-1)
中央高校基本科研业务费项目(2015QNA31)资助
