A GAN-Based Ensemble Model for Predicting the Demand of Shared Bikes in 5G Networks

The current unreasonable deployment mechanism of shared bikes makes it difficult to meet public demand. Accurate prediction of shared bikes demand can alleviate this problem, but current prediction models are limited by small data size and poor robustness, making them unable to provide sufficiently accurate prediction to be applied in real-life situations. In this work, we propose a generative adversarial networks (GAN)-based ensemble model (GANEM) for predicting the demand of shared bikes in 5G networks to solve the above problem. With the support of 5G networks, real-time transmission and storage of traffic data of shared bikes have become reality. We then use these data to train the GANEM model to have better generalization performance. The GANEM model first uses GAN to learn the data features of existing shared bikes demand to generate new training samples. We borrow the idea of ensemble learning, three benchmark predictors are integrated to fully exploit the association between explicit features, which can improve the robustness of the model. Excellent prediction results are achieved on two real datasets. The experimental results of our proposed GANEM model on two datasets are as follows, the values of MAE are 0.120 and 0.144, the values of MSE are 0.027 and 0.032, the values of RMSE are 0.164 and 0.181.