LSTCN: An Attention-based Deep Neural Network Model Combining LSTM and TCN for Cellular Network Traffic Prediction

In order to reduce the energy consumption of the cellular network and build a green communication network, it is very necessary to accurately predict the changes in the traffic load of the base station. In this paper, we propose a novel base station traffic load forecasting model, named LSTCN, which combining Long Short-Term Memory Network (LSTM) and Time Convolutional Network (TCN). We conducted experiments on real cellular network data and the experimental results show that the model proposed in this paper has higher prediction accuracy than previous methods.