dmTP: A Deep Meta-Learning Based Framework for Mobile Traffic Prediction

Deep learning technologies have been widely exploited to predict mobile traffic. However, individually training deep learning models for various traffic prediction tasks is not only time consuming but also unrealistic, sometimes due to limited traffic records. In this article, we propose a novel deep meta-learning based mobile traffic prediction framework, namely, dmTP, which can adaptively learn to learn the proper prediction model for each distinct prediction task from accumulated meta-knowledge of previously learned prediction tasks. In dmTP, we regard each mobile traffic prediction task as a base-task and adopt an LSTM network with a fixed structure as the base-learner for each base-task. In order to improve the base-learner's prediction accuracy and learning efficiency, we further employ an MLP as the meta-learner to find the optimal hyper-parameter value and initial training status for the base-learner of a new base-task according to its meta-features. Extensive experiments with real-world datasets demonstrate that while guaranteeing a similar or even better prediction accuracy, meta-learning in the proposed dmTP reduces the numbers of epochs and base-samples needed to train the base-learners by around 75 percent and 81 percent, respectively, as compared with the existing prediction models.