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Traffic forecasting is an essential problem in urban planning and computing.The complex dynamic spatial-temporal dependencies among traffic objects (e.g.,sensors and road segments) have been calling for highly flexible models;unfortunately, sophisticated models may suffer from poor robustness especiallyin capturing the trend of the time series (1st-order derivatives with time),leading to unrealistic forecasts. To address the challenge of balancingdynamics and robustness, we propose TrendGCN, a new scheme that extends theflexibility of GCNs and the distribution-preserving capacity of generative andadversarial loss for handling sequential data with inherent statisticalcorrelations. On the one hand, our model simultaneously incorporates spatial(node-wise) embeddings and temporal (time-wise) embeddings to account forheterogeneous space-and-time convolutions; on the other hand, it uses GANstructure to systematically evaluate statistical consistencies between the realand the predicted time series in terms of both the temporal trending and thecomplex spatial-temporal dependencies. Compared with traditional approachesthat handle step-wise predictive errors independently, our approach can producemore realistic and robust forecasts. Experiments on six benchmark trafficforecasting datasets and theoretical analysis both demonstrate the superiorityand the state-of-the-art performance of TrendGCN. Source code is available athttps://github.com/juyongjiang/TrendGCN.

Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting