HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting

• URL: http://arxiv.org/abs/2508.09184v1
• Date: Thu, 07 Aug 2025 14:18:18 GMT
• Title: HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting
• Authors: Zineddine Bettouche, Khalid Ali, Andreas Fischer, Andreas Kassler,
• Abstract summary: We present Hierarchical SpatioTemporal Mamba (HiSTM), which combines a dual spatial encoder with a Mamba-based temporal module and attention mechanism.<n>HiSTM employs selective state space methods to capture spatial and temporal patterns in network traffic.<n>We show that the HiSTM generalizes well across different datasets and improves in accuracy over longer time-horizons.
• Score: 0.7111641404908191
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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