计算机科学
利用
机制(生物学)
卡尔曼滤波器
卷积神经网络
人工智能
机器学习
国家(计算机科学)
数据挖掘
算法
计算机安全
认识论
哲学
作者
Hui Ma,Kai Yang,Man-On Pun
标识
DOI:10.1016/j.comcom.2022.10.023
摘要
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 modeling. 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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