Traffic forecasting in cellular networks using the LSTM RNN

In this work we design and implement a Neural Network that can identify recurrent patterns in various metrics which can be then used for cellular network traffic forecasting. Based on a custom architecture and memory, this Neural Network can handle prediction tasks faster and more accurately in real life scenarios. This approach offers a solution for service providers to enhance cellular network performance, by utilizing effectively the available resources. In order to provide a robust conclusion about the performance and precision of the proposed Neural Network, multiple predictions were made using the same data-set and the results were compared against other similar algorithms from the literature.