Prediction of Celestial Pole Offsets Based on Sliding Window and Bivariate Least Squares Fitting

Abstract

As an important component of Earth Orientation Parameters (EOP), the prediction of Celestial Pole Offsets (CPO) holds significant importance for missions such as deep space exploration. To explore a better CPO prediction algorithm that improves accuracy across different forecast spans, a CPO prediction algorithm is proposed based on a sliding window and bivariate least squares fitting. First, experiments determine an optimal sliding window of 900 days. Then, bivariate least squares fitting is performed on the selected 900-day historical data to complete extrapolation prediction. Then, bivariate least squares fitting is performed on the selected 900 day historical data to complete extrapolation prediction. Experimental results show that the proposed algorithm exhibits excellent accuracy. In comparisons with prediction results from participating teams in the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC), the algorithm's Mean Absolute Error (MAE) is superior to both ID154 and ID155. Team ID154 achieved the best dX prediction, while Team ID155 achieved the best dY prediction. Furthermore, the algorithm performs well not only on the EOP 14 C04 series but also on the newly released EOP 20 C04 series after the 2nd EOP PCC. Its prediction results are far better than those in the daily files published by the International Earth Rotation and Reference Systems Service (IERS). In terms of dX forecast accuracy, the MAE for the 10th, 30th, and 57th days were reduced by 53%, 59%, and 60%, respectively. In terms of dY forecast accuracy, the MAE for the 10th, 30th, and 57th days were reduced by 35%, 38%, and 42%, respectively.