Exploring Time Series Variability: A Training-Efficient Mobile Traffic Predictor

Mobile traffic forecasting is an important topic for mobile operators as accurate predictions are essential for en-hancing network efficiency. However, predicting mobile demand is difficult because it requires to distinguish different types of patterns. Recently, deep learning based approaches have been proven effective because of their ability to learn complex non-linear dependencies. Among different architectures, the models built on Recurrent Neural Network (RNN) show impressive performance in sequence modelling and they are widely used in mobile traffic forecasting; however, their rather long training time raises the issue of a high training cost for mobile operators. To better capture the latest trend of mobile demand, mobile op-erators would retrain the forecasting model every week using the recent observations; the training is very expensive and consumes a considerable amount of energy considering that massive cells are managed by mobile operators. For mobile operators, it is always desirable to reduce the training cost without reducing the forecasting performance. To address this challenge, in this paper we propose a pure Fe-layer deep learning predictor allowing the mobile operators to obtain excellent forecasting performance along with very low training cost. The network is composed of three feature extraction layers, containing RNN -inspired blocks learning features that are robust to unexpected local variations; these features are then used to make predictions in the last two layers. Extensive experiments are conduct on a real-world dataset, showing that our model obtains similar prediction performance as the state-of-the-art RNN-based baseline model, while reuuiring only 1 % of its trainina time.