  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 especially
in capturing the trend of the time series (1st-order derivatives with time),
leading to unrealistic forecasts. To address the challenge of balancing
dynamics and robustness, we propose TrendGCN, a new scheme that extends the
flexibility of GCNs and the distribution-preserving capacity of generative and
adversarial loss for handling sequential data with inherent statistical
correlations. On the one hand, our model simultaneously incorporates spatial
(node-wise) embeddings and temporal (time-wise) embeddings to account for
heterogeneous space-and-time convolutions; on the other hand, it uses GAN
structure to systematically evaluate statistical consistencies between the real
and the predicted time series in terms of both the temporal trending and the
complex spatial-temporal dependencies. Compared with traditional approaches
that handle step-wise predictive errors independently, our approach can produce
more realistic and robust forecasts. Experiments on six benchmark traffic
forecasting datasets and theoretical analysis both demonstrate the superiority
and the state-of-the-art performance of TrendGCN. Source code is available at
https://github.com/juyongjiang/TrendGCN.
