TDANet: An Efficient Solution For Short-Term Mobile Traffic Forecasting

Mobile traffic forecasting is crucial for optimizing the network configuration and improving Quality-of-Service (QoS); accurate mobile traffic predictions can help the network operators better configure the network and adapt to the trend of mobile demand. However, improving mobile traffic forecasting is challenging as traffic patterns exhibit strong periodicity while also retaining a certain level of randomness; this makes it difficult to model the sequence correlation. To obtain better mobile traffic predictions, we proposed a deep learning-based predictor called Temporal Dynamics Aware Network (TDANet). TDANet is carefully designed to extract both the global and the local temporal patterns employing recurrent and attention components; a linear module is employed to make the predictions more sensitive to the magnitude of mobile traffic, which makes the model better able to capture fast dynamics. Extensive experiments are conducted on a real-world mobile traffic dataset, and the results show that TDANet outperforms all the baseline models on all evaluation metrics, including both accuracy and complexity.