Forecasting Framework for Mobile Networks Based on Automatic Feature Selection

Traditionally, mobile network management has been based on reactive systems, where corrective actions are taken when a failure or a suboptimal network performance is detected. However, the requirements of low latency and high throughput associated with 5G new services, make these reactive systems insufficient to manage the new needs of users. Thus, the focus of mobile network management has changed to a proactive approach, where preventive actions are taken to avoid failures and suboptimal network performance. Moreover, in 5G, management algorithms are expected to make use of large amounts of information sources. The use of this large amount of information and the need to use historical data to generate forecasting models, may cause a loss of accuracy. To address these challenges, a forecasting framework is proposed in this work. The framework is based on the use of automatic feature selection techniques in both spatial and temporal dimensions as a pre-prediction stage. In this way, the proposed framework can improve the accuracy of other prediction schemes when using large amounts of data as input. The proposed scheme has been tested using different forecasting techniques. In addition, the impact of the proposed framework on predictions of different future lags has been analyzed.