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A Polar Motion Prediction Model Based on Improved LS and Component Correlation Fusion
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Yaxin YAO1, 2, Feiyu HAO3, *, Tong CHENG1, 2, Qianxin WANG1, 2
Journal of Geodesy and Geodynamics | 2026, 46(7) : 834 - 841
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Journal of Geodesy and Geodynamics | 2026, 46(7): 834-841
A Polar Motion Prediction Model Based on Improved LS and Component Correlation Fusion
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Yaxin YAO1, 2, Feiyu HAO3, *, Tong CHENG1, 2, Qianxin WANG1, 2
Affiliations
  • 1 School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
  • 2 Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin 541004, China
  • 3 State-owned Assets Investment & Management Group Co Ltd of Xuzhou, Xuzhou 221000, China
Published: 2026-07-15 doi: 10.14075/j.jgg.2025.11.376
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To explore high-accuracy autonomous polar motion (PM) prediction using purely data-driven methods, we propose a hybrid model named PSO-FFRLS+MAR (forgetting factor recursive least squares optimized by particle swarm optimization and multivariate autoregressive). This model dynamically weights historical data to track slow system variations and jointly models the co-oscillations of PMX and PMY residuals. Experiments show that during the 1 to 90 days prediction horizon, the proposed model outperforms mainstream models in both accuracy and stability, with particularly significant improvements for PMY (compared with the classical LS+AR model, a 35.4% reduction in MAE). Day-by-day validation confirms the statistical significance of these improvements. The greater enhancement in PMY accuracy may be physically attributed to the model's enhanced ability to capture the strong seasonal atmospheric excitation that dominates this component. This study provides a high-performance data-driven method for medium- to short-term autonomous prediction of Earth orientation parameters and, through error analysis, links the model's physical mechanisms with geophysical angular momentum excitation functions.

polar motion(PM)  /  forgetting factor recursive least squares(FFRLS)  /  multivariate autoregressive(MAR)  /  PMX-PMY correlation  /  medium- to short-term prediction
要亚鑫, 郝飞宇, 程彤, 王潜心. 融合改进LS与分量相关性的极移预报模型. 大地测量与地球动力学, 2026 , 46 (7) : 834 -841 . DOI: 10.14075/j.jgg.2025.11.376
Yaxin YAO, Feiyu HAO, Tong CHENG, Qianxin WANG. A Polar Motion Prediction Model Based on Improved LS and Component Correlation Fusion[J]. Journal of Geodesy and Geodynamics, 2026 , 46 (7) : 834 -841 . DOI: 10.14075/j.jgg.2025.11.376
Year 2026 volume 46 Issue 7
24
8
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doi: 10.14075/j.jgg.2025.11.376
  • Receive Date:2025-11-03
  • Online Date:2026-10-30
  • Published:2026-07-15
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  • Received:2025-11-03
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Affiliations
    1 School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
    2 Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin 541004, China
    3 State-owned Assets Investment & Management Group Co Ltd of Xuzhou, Xuzhou 221000, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
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Percentage of
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种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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