This paper proposes an interpretable user-behavior-based (UBB) network traffic prediction (NTP) method. Based on user behavior, a weekly traffic demand profile can be naturally sorted into three categories, i.e., weekday, Saturday, and Sunday. For each category, the traffic pattern is divided into three components which are mainly generated in three time periods, i.e., morning, afternoon, and evening. Each component is modeled as a normal-distributed signal. Numerical results indicate the UBB NTP method matches the practical wireless traffic demand very well. Compared with existing methods, the proposed UBB NTP method improves the computational efficiency and increases the predictive accuracy.

翻译：本文提出了一种可解释的基于用户行为的网络流量预测方法。根据用户行为，周流量需求分布可自然分为三类：工作日、周六和周日。针对每一类别，流量模式被分解为三个主要产生于三个时间段的组成部分，即上午、下午和晚间。每个组成部分均被建模为正态分布信号。数值结果表明，该基于用户行为的网络流量预测方法与实际无线流量需求高度吻合。与现有方法相比，所提出的基于用户行为的网络流量预测方法提高了计算效率并提升了预测准确率。