Adaptive Dual-View WaveNet for urban spatial-temporal event prediction

Spatial-temporal event prediction is a particular task for multivariate time series forecasting. Therefore, the complex entangled dynamics of space and time need to be considered. This task is an essential but crucial loop in future smart cities construction, which can be widely applied in urban traffic management, disaster monitoring and mobility analysis. In recent years, video-like spatial-temporal modelling has been the most common approach in many deep learning models. However, the video-like modelling approach cannot consider some latent region-wise correlations other than geographic spatial distance information. To overcome the limitation, we propose a novel neural network framework, Adaptive Dual-View WaveNet (ADVW-Net), for the urban spatial-temporal event prediction. By integrating the spatial representations from Convolutional Neural Network (CNN) and that from adaptive Graph convolutional neural network (GCN), our proposed model can capture not only the geographic correlations but also some latent region-wise dependencies from the input data. In addition, the effective architecture, WaveNet, can be transferred to region-wise spatial-temporal prediction scenarios for long-range temporal dependencies learning. Experimental results on three urban datasets demonstrate the superior performance of our proposed model.(c) 2021 Elsevier Inc. All rights reserved.

Automated summary

Spatial-temporal event prediction is crucial for future smart cities construction, with recent deep learning models commonly using video-like spatial-temporal modelling. However, limitations exist in considering latent region-wise correlations, prompting the proposal of the Adaptive Dual-View WaveNet (ADVW-Net) framework to capture both geographic correlations and region-wise dependencies. The integration of Convolutional Neural Network (CNN) and adaptive Graph Convolutional Neural Network (GCN) representations, along with the WaveNet architecture, allows for effective learning of long-range temporal dependencies in urban spatial-temporal event prediction.