A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Abstract

Short-term (up to 30 days) predictions of Earth Rotation Parameters (ERPs) such as Polar Motion (PM: PMX and PMY) play an essential role in real-time applications related to high-precision reference frame conversion. Currently, least squares (LS) + auto-regressive (AR) hybrid method is one of the main techniques of PM prediction. Besides, the weighted LS + AR hybrid method performs well for PM short-term prediction. However, the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model. In this study, we have derived a modified stochastic model for the LS + AR hybrid method, namely the weighted LS + weighted AR hybrid method. By using the PM data products of IERS EOP 14 C04, the numerical results indicate that for PM short-term forecasting, the proposed weighted LS + weighted AR hybrid method shows an advantage over both the LS + AR hybrid method and the weighted LS + AR hybrid method. Compared to the mean absolute errors (MAEs) of PMX/PMY short-term prediction of the LS + AR hybrid method and the weighted LS + AR hybrid method, the weighted LS + weighted AR hybrid method shows average improvements of 6.61%/12.08% and 0.24%/11.65%, respectively. Besides, for the slopes of the linear regression lines fitted to the errors of each method, the growth of the prediction error of the proposed method is slower than that of the other two methods.