“…To date, a number of Satellite Clock Bias (SCB) analysis and prediction models or algorithms, such as the Quadratic Polynomial (QP) model (Huang et al, 2014; Wang et al, 2016), Spectrum Analysis (SA) (Heo et al, 2010; Senior et al, 2008), Grey system Model (GM) (Yuan et al, 2008; Zheng et al, 2008), Auto Regressive Integrated Moving Average (ARIMA) model (Xi et al, 2014; Stein and Evans, 1990), Kalman Model (Galleani and Tavella, 2010; Davis et al, 2012; Pratt et al, 2013), Least Square Support Vector Machine (LS-SVM) model (Liu et al, 2013), Wavelet Neural Network (WNN) model (Ai et al, 2016; Wang et al, 2017), etc. In addition, combination prediction methods were also proposed by combining typical multiple models (Lei et al, 2014; Wang et al, 2011; Xu et al, 2016). However, existing models or algorithms generally do not take fully into account the influences of factors such as periodic items, sampling length, stochastic items and so on, which result in low precision and usability.…”