小数据下精准预测网络流量，用元学习快速适配新场景。
MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction
- 基于多模态元学习框架，端到端训练并迁移知识。
- 仅需少量真实数据即可实现高精度预测，误差显著降低。
- 适合资源受限的实时网络管理与智能运维场景。
网络流量预测对缓解拥塞、提升用户体验具有重要意义。现有方法在数据充足时表现良好，但在小样本条件下仍面临挑战。为此，我们提出一种名为MetaSTNet的深度学习模型，基于多模态元学习框架构建。该模型在仿真环境中训练，将元知识迁移到真实场景，仅需少量真实数据即可快速适应新任务并获得准确预测。此外，采用交叉置信预测（cross conformal prediction）校准预测区间，确保可靠性。在多个真实数据集上的实验验证了MetaSTNet在效率与有效性方面的优势。
原文摘要 · Abstract (English)
Network traffic prediction techniques have attracted much attention since they are valuable for network congestion control and user experience improvement. While existing prediction techniques can achieve favorable performance when there is sufficient training data, it remains a great challenge to make accurate predictions when only a small amount of training data is available. To tackle this problem, we propose a deep learning model, entitled MetaSTNet, based on a multimodal meta-learning framework. It is an end-to-end network architecture that trains the model in a simulator and transfers the meta-knowledge to a real-world environment, which can quickly adapt and obtain accurate predictions on a new task with only a small amount of real-world training data. In addition, we further employ cross conformal prediction to assess the calibrated prediction intervals. Extensive experiments have been conducted on real-world datasets to illustrate the efficiency and effectiveness of MetaSTNet.
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