标题： HiSTM：用于细胞交通预测的分层时空马尔可夫模型 显示英文标题

标题： HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting

摘要： 单元流量预测对于网络规划、资源分配或跨单元的负载平衡流量至关重要。 然而，由于用户移动性导致的复杂空间和时间模式，准确预测是困难的。 现有的基于人工智能的流量预测模型通常在准确性和计算效率之间进行权衡。 我们提出了分层时空马布姆（HiSTM），它结合了双空间编码器与基于马布姆的时间模块和注意力机制。 HiSTM采用选择性状态空间方法来捕捉网络流量中的空间和时间模式。 在我们的评估中，我们使用一个真实世界的数据集将HiSTM与多个基线进行比较，结果显示在MAE指标上比STN基线提高了29.4%，同时使用的参数减少了94%。 我们证明了HiSTM在不同数据集上具有良好的泛化能力，并且在更长的时间范围内提高了准确性。 显示英文摘要

摘要： 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.