Abstract
This paper presents a short-term traction-load forecasting method that fuses optimization-driven dual-scale decomposition and multiscale information fusion (ODE-MMF) with TimeXer-Mamba to address non-stationary prediction difficulties caused by intermittent and volatile traction loads. A correlation analysis module is first constructed for adjacent feeding sections, where mutual information quantifies cross-arm load transfer induced by train operations and extracts key spatial features. In the ODE-MMF signal processing module, an improved whale migration algorithm searches for the optimal parameters of optimization-driven dual-scale decomposition, enabling multiscale decomposition of load features. Multiscale transfer entropy is then used to measure information flow among decomposed components, and highly redundant components are adaptively merged into complementary feature subsequences. In the TimeXer-Mamba prediction module, TimeXer enhances exogenous variables such as holidays, whereas Mamba captures long-range dependencies through the selective state-space model. A gated fusion mechanism integrates the two representations, after which the merged subsequences are predicted in parallel and reconstructed to obtain the final forecast. Experiments conducted on real-world traction-load data demonstrate that the proposed model consistently outperforms all evaluated baselines. Relative to the best-performing baseline, LSTM-Transformer, it achieves reductions of 9.61%, 9.32%, and 9.81% in mean absolute error, root mean square error, and mean absolute percentage error, respectively, while maintaining high computational efficiency and demonstrating strong potential for practical deployment in railway power supply systems.