STM2CN: A Multi-graph Attention-based Framework for Sensor Data Prediction in Smart Cities

Zhiling Jin, Jing Xu, Ruiqi Huang, Wei Shao, Xiao Xiao

Abstract: Accurate long-term predictions help governments make decisions and residents travel, which is essential for the development of smart cities. Fortunately, due to the deployment of low-cost sensors, a large amount of time-series data such as parking availability data and air quality data has been stored, which makes it possible for long-term predictions. Many state-of-the-art studies based on multiple graphs have shown excellent performance in long-term prediction tasks. However, few previous studies employ multiple attention mechanisms to their models based on multi-graphs and thus fail to comprehensively capture the dynamic spatio-temporal correlations as well as the inner relationships among graphs. To this end, we propose a spatio-temporal multi-attention multi-graph convolutional network (STM2CN) framework for long-term prediction. We applied four different graphs to mine the potential contextual relationships and employed three attention mechanisms to capture the multiple graph and spatio-temporal correlations. Experiments on two large-scale real-world datasets demonstrate that the proposed STM2CN framework outperformed the state-of-the-art baselines.