Abstract The multivariable regression model combined with least squares is proposed to forecast polar motion. The residual error prediction model is constructed by using the PMX residual and PMY residuals, which not only utilizes the correlation information of the PMX residual and the PMY residual, but also uses the correlation information between the PMX residual and the PMY residual. By comparison with the prediction results of LS+AR model, it is proven that the prediction result of LS+MAR model is better than that of LS+AR model, and the superiority of LS+MAR model is also proven. In addition, by comparison with the prediction results of EOP_PCC, it is proven that the LS+MAR model can obtain the prediction results equivalent to the best international prediction accuracy in short-term polar motion prediction.
WANG Zhiwen,WANG Qianxin,HE Yilei et al. A New Method to Predict Pole Shift Based on the Correlation Between PMX and PMY[J]. jgg, 2017, 37(11): 1178-1182.
WANG Zhiwen,WANG Qianxin,HE Yilei et al. A New Method to Predict Pole Shift Based on the Correlation Between PMX and PMY[J]. jgg, 2017, 37(11): 1178-1182.