HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting

TL;DR

HiSTM is a novel hierarchical model that effectively captures complex spatiotemporal patterns in cellular traffic data, significantly improving forecasting accuracy while reducing computational complexity.

Contribution

The paper introduces HiSTM, a new hierarchical spatiotemporal model combining dual encoders and a Mamba-based module for enhanced cellular traffic prediction.

Findings

29.4% MAE improvement over baseline

94% fewer parameters than existing models

Effective across multiple datasets and longer horizons

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.