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HiSTM: Hierarchical SpatioTemporal Mamba for cellular traffic forecasting

  • 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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Metadaten
Author:Zineddine Bettouche, Khalid Ali, Andreas Fischer, Andreas Kassler
URN:urn:nbn:de:bvb:384-opus4-1252496
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/125249
Parent Title (English):3rd Workshop on Machine Learning in Networking (MaLeNe), co-located with the 6th International Conference on Networked Systems (NetSys 2025), Ilmenau, Germany, September 1, 2025: proceedings
Publisher:Universität Augsburg
Place of publication:Augsburg
Editor:Michael SeufertORCiDGND, Andreas Blenk, Björn Richerzhagen
Type:Conference Proceeding
Language:English
Date of Publication (online):2025/09/15
Year of first Publication:2025
Publishing Institution:Universität Augsburg
Release Date:2025/09/16
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Vernetzte Systeme und Kommunikationsnetze
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)

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