A Hybrid LSVMD-Informer Framework for High-Accuracy Polar Motion Time Series Prediction

Abstract Polar motion prediction is a core technology supporting geomagnetic navigation and space environment monitoring. It directly impacts application efficacy in critical fields, including spacecraft orbit control, geomagnetic field modelling, and space hazard early warning. Due to simplified assumptions, traditional empirical prediction models suffer from significant accuracy degradation in long-term extrapolation forecasts. At the same time, existing deep learning methods are constrained by their limited ability to capture multi-scale features, making effective modelling challenging. This paper proposes a multi-scale hybrid prediction model named LSVMD+Informer to address these issues. The model innovatively integrates three key techniques: Least Squares (LS) periodic decomposition, Variational Mode Decomposition (VMD) for residual feature extraction, and the Informer method for long-sequence time-series prediction. Combining these approaches constructs a multi-scale feature decoupling and hierarchical temporal modelling framework. This integration effectively resolves the coupling problem of multi-scale information under complex patterns.Experiments were conducted using high-precision observational data from 2002 to 2022, with rolling predictions performed for 2022–2025 data. A three-dimensional error analysis system was established to compare the model with IERS Bulletin A and the LS+Informer baseline model. The results show that LSVMD+Informer outperforms the Bulletin A model with significant accuracy improvements. The PMX achieves an average accuracy gain of 20.20%, reaching up to 28.49% in some cases. Similarly, the average improvement for the PMY is 26.28%, with a maximum increase of 33.35%. The results demonstrate that LSVMD+Informer significantly improves the accuracy and robustness of polar motion prediction. This method exhibits precise capabilities in capturing complex periodic features, proving its effectiveness in modelling intricate temporal patterns.