Machine Learning (ML) is the dominating solution for the implementation of Self-Organizing Networks (SON), which automate mobile network management. However, the data scarcity derived from the reluctance of operators complicates the necessary training phase ML algorithms. In this letter a method to generate artificial Key Performance Indicators (KPIs) time series is proposed considering their time-dependent behaviour. The data is modelled and categorised according to the time of the day and the data models are adapted with statistical copulas to create samples which present interrelation among different KPIs. Finally, results obtained from a real mobile network are presented.

中文翻译：

---

具有现实时间相关行为的人工 KPI 生成方法


机器学习 (ML) 是实现自组织网络 (SON) 的主要解决方案，可实现移动网络管理自动化。然而，由于操作员不愿意而导致的数据稀缺使必要的训练阶段机器学习算法变得复杂。在这封信中，考虑到人工关键绩效指标 (KPI) 时间序列的时间依赖性行为，提出了一种生成方法。根据一天中的时间对数据进行建模和分类，并使用统计联结函数调整数据模型以创建呈现不同 KPI 之间相互关系的样本。最后，给出了从真实移动网络获得的结果。