用分层结构提升蜂窝流量预测精度与效率
HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting
- 双空间编码器+Mamba时序模块，捕捉复杂时空模式
- 相比STN基线，MAE降低29.4%，参数量减少94%
- 适用于长时序预测，跨数据集泛化能力强
蜂窝网络流量预测对网络规划、资源分配和负载均衡至关重要。由于用户移动性带来的复杂时空模式，准确预测极具挑战。现有AI模型常在精度与计算效率间权衡。本文提出分层时空马尔可夫（HiSTM）模型，结合双空间编码器与基于Mamba的时序模块及注意力机制，利用选择性状态空间方法捕捉网络流量中的时空特征。在真实数据集上的评估显示，相较于STN基线，HiSTM实现29.4%的MAE降低，同时仅需94%更少的参数。实验表明，HiSTM在不同数据集上具有良好泛化能力，且在长时序预测中持续提升精度。
原文摘要 · Abstract (English)
Cellular traffic forecasting is essential for network planning, resource allocation, or load-balancing traffic across cells. However, accurate forecasting is difficult due to intricate spatial and temporal patterns that exist due to the mobility of users. Existing AI-based traffic forecasting models often trade-off accuracy and computational efficiency. We present Hierarchical SpatioTemporal Mamba (HiSTM), which combines a dual spatial encoder with a Mamba-based temporal module and attention mechanism. HiSTM employs selective state space methods to capture spatial and temporal patterns in network traffic. In our evaluation, we use a real-world dataset to compare HiSTM against several baselines, showing a 29.4% MAE improvement over the STN baseline while using 94% fewer parameters. We show that the HiSTM generalizes well across different datasets and improves in accuracy over longer time-horizons.
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