  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.
