中文摘要：

为了更好地反映钟差特性并提高其预报精度，建立一种能够同时考虑星载原子钟物理特性、钟差周期性变化与随机性变化特点的钟差预报模型．首先采用附有周期项的二次多项式模型进行拟合提取卫星钟差（SatelliteClockBias，SCB）的趋势项与周期项，然后根据拟合残差的特点采用时间序列ARIMAfAuto-RegressiveIntegratedMovingAverage）模型对残差进行建模；最后将两种模型的预报结果结合得到最终钟差预报值．使用IGSfInternationalGNSSService）精密钟差数据进行预报试验，将新方法与二次多项式模型、灰色模型及ARIMA模型进行对比，证明了新方法能够更高精度地预报卫星钟差，且可以一定程度上改善ARIMA存在模型识别与定阶不准的不足．

英文摘要：

In order to better express the characteristics of satellite clock bias （SCB） and improve SCB prediction precision, this paper proposed a new SCB prediction model which can take physical characteristics of space-borne atomic clock, the cyclic variation, and random part of SCB into consideration. First, the new model employs a quadratic polynomial model with periodic items to fit and extract the trend term and cyclic term of SCB; then based on the characteristics of fitting residuals, a time series ARIMA （Auto-Regressive Integrated Moving Average） model is used to model the residuals; eventually, the results from the two models are combined to obtain final SCB prediction values. At last, this paper uses precise SCB data from IGS （International GNSS Service） to conduct prediction tests, and the results show that the proposed model is effective and has better prediction performance compared with the quadratic polynomial model, grey model, and ARIMA model. In addition, the new method can also overcome the insufficiency of the ARIMA model in model recognition and order determination.