With rapid expansion of cellular networks and the proliferation of mobile devices, cellular traffic data exhibits complex temporal dynamics and spatial correlations, posing challenges to accurate traffic prediction. Previous methods often focus predominantly on temporal modeling or depend on predefined spatial topologies, which limits their ability to jointly model spatio-temporal dependencies and effectively capture periodic patterns in cellular traffic. To address these issues, we propose a cellular traffic prediction framework that integrates spatio-temporal modeling with time-frequency analysis. First, we construct a spatial modeling branch to capture inter-cell dependencies through an attention mechanism, minimizing the reliance on predefined topological structures. Second, we build a time-frequency modeling branch to enhance the representation of periodic patterns. Furthermore, we introduce an adaptive-scale LogCosh loss function, which adjusts the error penalty based on traffic magnitude, preventing large errors from dominating the training process and helping the model maintain relatively stable prediction accuracy across different traffic intensities. Experiments on three open-sourced datasets demonstrate that the proposed method achieves prediction performance superior to state-of-the-art approaches.

翻译：随着蜂窝网络的快速扩张和移动设备的普及，蜂窝流量数据呈现出复杂的时序动态与空间相关性，为精准流量预测带来挑战。现有方法通常侧重于时序建模或依赖于预定义的空间拓扑结构，这限制了其联合建模时空依赖关系并有效捕捉蜂窝流量周期性模式的能力。为解决这些问题，我们提出一种将时空建模与时频分析相结合的蜂窝流量预测框架。首先，我们构建空间建模分支，通过注意力机制捕捉小区间的依赖关系，从而最小化对预定义拓扑结构的依赖。其次，我们建立时频建模分支以增强周期性模式的表征能力。此外，我们引入自适应尺度的LogCosh损失函数，该函数根据流量大小调整误差惩罚，防止大误差主导训练过程，并帮助模型在不同流量强度下保持相对稳定的预测精度。在三个开源数据集上的实验表明，所提方法的预测性能优于当前最先进的方法。