The Least-squares extrapolation of harmonic models and autoregressive (LS + AR) prediction is currently considered to be one of the best prediction model for polar motion parameters. In this method, LS fitting residuals are treated as data to train an AR model. But it is readily known that using too many data will result in learning a badly relevant AR model, implying increasing the model bias. It can also be possible that using too few data will result in a lower estimation accuracy of the AR model, implying increasing the model variance. So selecting data is a critical issue to compromise between bias and variance, and hence to obtain a model with optimized prediction performance. In this paper, an experimental study is conducted to check the effect of different data volume on the final prediction performance and hence to select an optimal data portion for AR model. The earth orientation parameters products released by the International Earth Rotation and Reference Systems Service were used as primary data to predict changes in polar motion parameters over spans of 1–500 days for 800 experiments. The experimental results showed that although the short term prediction were not ameliorated, but the method that the AR model parameters calculated by appropriate data volume can effectively improve the accuracy of long-term prediction of polar motion.

中文翻译：

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选择数据以进行极运动预测中的自回归建模

谐波模型和自回归（LS + AR）预测的最小二乘外推目前被认为是极运动参数的最佳预测模型之一。在这种方法中，将LS拟合残差视为训练AR模型的数据。但众所周知，使用太多数据将导致学习不良的AR模型，这意味着模型偏差会增加。使用过少的数据也可能导致AR模型的估算准确性降低，这意味着模型方差增加。因此，选择数据是折衷偏差和方差，从而获得具有最佳预测性能的模型的关键问题。在本文中，进行了一项实验研究，以检查不同数据量对最终预测性能的影响，从而为AR模型选择最佳数据部分。国际地球自转和参考系统服务公司发布的地球方向参数产品被用作主要数据，以预测800个实验跨1–500天的极地运动参数的变化。实验结果表明，虽然短期预测没有得到改善，但是通过适当的数据量计算AR模型参数的方法可以有效地提高极地运动的长期预测精度。国际地球自转和参考系统服务公司发布的地球方向参数产品被用作主要数据，以预测800个实验跨1–500天的极地运动参数的变化。实验结果表明，虽然短期预测没有得到改善，但是通过适当的数据量计算AR模型参数的方法可以有效地提高极地运动的长期预测精度。国际地球自转和参考系统服务公司发布的地球方向参数产品被用作主要数据，以预测800个实验跨1–500天的极地运动参数的变化。实验结果表明，虽然短期预测没有得到改善，但是通过适当的数据量计算AR模型参数的方法可以有效地提高极地运动的长期预测精度。