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.
