Traffic prediction is a classical time series prediction which has been investigated in different domains, but most existing models are proposed based on limited time or spatial scale. Mobile cellular network traffic prediction is of paramount importance for quality-of-service (QoS) and power management of the cellular base stations, especially in the 5G era. Through the statistical analysis of the real historical traffic data obtained in a city scale spanning across multiple months, this paper makes an in-depth study of the temporal characteristics and behavior rules of the model data traffic. Considering that the time series data show different changing rules under the different time dimensions, spatial dimensions and independent dimensions, a multi-dimensional recurrent neural network (MDRNN) prediction model is established to predict the future cell traffic volume over various temporal and spatial dimensions. The data of this paper are trained and tested over real data of a city, and the granularity of the proposed prediction model can be drilled down to the cell level. Compared with the traditional trend fitting method, the proposed model achieves mean absolute percentage error (MAPE) reduction of 6.56%, and provides guidance for energy efficiency optimization and power consumption reduction of base stations in various temporal and spatial dimensions.

中文翻译：

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城市尺度小区交通多维预测模型

交通预测是一种经典的时间序列预测，已在不同领域进行了研究，但现有的大多数模型都是基于有限的时间或空间尺度提出的。移动蜂窝网络流量预测对于蜂窝基站的服务质量 (QoS) 和电源管理至关重要，尤其是在 5G 时代。通过对跨越多个月的城市尺度获取的真实历史交通数据进行统计分析，深入研究模型数据交通的时间特征和行为规律。考虑到时间序列数据在不同的时间维度、空间维度和独立维度下表现出不同的变化规律，建立了多维递归神经网络（MDRNN）预测模型，以预测未来各个时间和空间维度上的小区交通量。本文的数据是在一个城市的真实数据上训练和测试的，所提出的预测模型的粒度可以下钻到细胞级别。与传统趋势拟合方法相比，该模型实现了平均绝对百分比误差（MAPE）降低6.56%，为基站在各个时空维度的能效优化和功耗降低提供了指导。