A new medium-long term polar motion prediction method based on sliding average within difference series

In response to the problem that the input data and combination method of existing polar motion (PM) prediction methods are relatively single, which leads to the limited satisfaction of PM prediction accuracy by major satellite navigation orbiting systems and deep space exploration projects. This study borrows the idea of differential method and proposes to push back the forecast after selecting several samples within the PM Y, X and Y-X series by sliding average. In the constructed new series, the high-frequency terms are effectively attenuated. Then, the pushing back forecasts are combined in pairs with those of the traditional method. After least-squares extrapolation and autoregressive (LS + AR) modeling, the optimal combination was found. Among them, the prediction of PMX is obtained by subtracting the forecast of PMY of traditional method and the prediction of PM(Y-X) of the sliding average method, the forecast of PMY is obtained by adding the forecast of PMX of the sliding average method and the forecast of PM(Y-X) of the traditional method. The results of the 418-week hindcast experiment from 2012 to 2021 show that the proposed method has a greater improvement than the traditional method, and the corresponding 1-365-day mean absolute error (MAE) are improved by 31.46% and 21.11%, respectively, on average. It has certain advantages over the IERS Bulletin-A in the medium-long term, and the 150-day lead time predictions, the MAE of PMX and PMY were 14.678 and 17.232 mas, respectively, which were less than the 17.833 and 20.769 mas predicted by IERS Bulletin A. This not only verifies that the stability and ability of the proposed method have some competitive ability, but also provides new ideas for other time-series forecasting studies.

Automated summary

This study proposes a method that combines the ideas of differential method and sliding average to improve the accuracy of polar motion prediction. The results show that the proposed method outperforms the traditional method and has advantages over the IERS Bulletin-A in the medium-long term. The method also provides new ideas for other time-series forecasting studies.