用注意力机制建模蜂窝流量时空特征，提升预测精度。
Cellular Traffic Prediction via Deep State Space Models with Attention Mechanism
- 结合卷积网络与注意力机制捕捉邻近小区间空间动态。
- 引入卡尔曼滤波建模时间序列，显著优于现有方法。
- 可融合社交活动等辅助信息，适合网络优化场景。
蜂窝流量预测对运营商资源调度和决策至关重要。流量具有高度动态性，受多种外部因素影响，常导致预测精度下降。本文提出一种端到端框架，包含两种变体，可显式刻画相邻基站间流量的时空模式。通过带注意力机制的卷积神经网络捕获空间动态，利用卡尔曼滤波进行时间建模，并能充分融合社交活动等辅助信息以提升性能。在三个真实数据集上进行了广泛实验，结果表明所提模型在预测精度上优于当前主流机器学习方法。
原文摘要 · Abstract (English)
Cellular traffic prediction is of great importance for operators to manage network resources and make decisions. Traffic is highly dynamic and influenced by many exogenous factors, which would lead to the degradation of traffic prediction accuracy. This paper proposes an end-to-end framework with two variants to explicitly characterize the spatiotemporal patterns of cellular traffic among neighboring cells. It uses convolutional neural networks with an attention mechanism to capture the spatial dynamics and Kalman filter for temporal modelling. Besides, we can fully exploit the auxiliary information such as social activities to improve prediction performance. We conduct extensive experiments on three real-world datasets. The results show that our proposed models outperform the state-of-the-art machine learning techniques in terms of prediction accuracy.
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