Dynamic Recency-Weighted Multi-Scale PatchTST with Physically Motivated Statistical Anchors for Robust BDS-3 Clock Bias Prediction

High-precision satellite clock offset prediction is a core prerequisite for the BeiDou-3 Global Navigation Satellite System to achieve precise single-point positioning and timing. However, because of space radiation and the physical aging of the clock itself, the operational state of onboard atomic clocks exhibits a high degree of physical heterogeneity and time-varying drift characteristics. Traditional physical models struggle to capture complex nonlinear residuals, while existing deep learning methods often face boundary discontinuities caused by baseline separation when handling long-sequence forecasts. Furthermore, channel crosstalk in multivariate prediction and insufficient sensitivity to dynamic multiscale features limit the robustness of long-term predictions. To address these issues, this paper proposes a clock offset prediction architecture that integrates physically motivated statistical constraints with dynamic adaptive feature learning. Extensive experiments conducted using real BDS-3 precise clock difference products provided by Wuhan University demonstrate that the proposed method effectively mitigates the performance degradation often observed in existing models on heterogeneous satellites during the evaluated period. In the 24-h extrapolation task, the architecture achieved an average root-mean-square error as low as 0.507 ns, significantly improving prediction accuracy. It outperformed mainstream physical models and advanced deep learning baseline algorithms, providing a promising framework with good interpretability for high-precision clock error forecasting under dynamic space weather conditions.