Sim-MSTNet: sim2real based Multi-task SpatioTemporal Network Traffic Forecasting

• URL: http://arxiv.org/abs/2601.21384v1
• Date: Thu, 29 Jan 2026 08:20:08 GMT
• Title: Sim-MSTNet: sim2real based Multi-task SpatioTemporal Network Traffic Forecasting
• Authors: Hui Ma, Qingzhong Li, Jin Wang, Jie Wu, Shaoyu Dou, Li Feng, Xinjun Pei,
• Abstract summary: We propose Sim-MSTNet, a simulator for network traffic forecasting based on the sim2real approach.<n>Our method generates synthetic data effectively addressing the issue of poor generalization caused by data scarcity.<n>Our experiments show that Sim-MSTNet consistently outperforms state-of-the-art baselines.
• Score: 12.102656041220783
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Network traffic forecasting plays a crucial role in intelligent network operations, but existing techniques often perform poorly when faced with limited data. Additionally, multi-task learning methods struggle with task imbalance and negative transfer, especially when modeling various service types. To overcome these challenges, we propose Sim-MSTNet, a multi-task spatiotemporal network traffic forecasting model based on the sim2real approach. Our method leverages a simulator to generate synthetic data, effectively addressing the issue of poor generalization caused by data scarcity. By employing a domain randomization technique, we reduce the distributional gap between synthetic and real data through bi-level optimization of both sample weighting and model training. Moreover, Sim-MSTNet incorporates attention-based mechanisms to selectively share knowledge between tasks and applies dynamic loss weighting to balance task objectives. Extensive experiments on two open-source datasets show that Sim-MSTNet consistently outperforms state-of-the-art baselines, achieving enhanced accuracy and generalization.

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