  Deep learning models for traffic forecasting often assume the residual is
independent and isotropic across time and space. This assumption simplifies
loss functions such as mean absolute error, but real-world residual processes
often exhibit significant autocorrelation and structured spatiotemporal
correlation. This paper introduces a dynamic regression (DR) framework to
enhance existing spatiotemporal traffic forecasting models by incorporating
structured learning for the residual process. We assume the residual of the
base model (i.e., a well-developed traffic forecasting model) follows a
matrix-variate seasonal autoregressive (AR) model, which is seamlessly
integrated into the training process through the redesign of the loss function.
Importantly, the parameters of the DR framework are jointly optimized alongside
the base model. We evaluate the effectiveness of the proposed framework on
state-of-the-art (SOTA) deep traffic forecasting models using both speed and
flow datasets, demonstrating improved performance and providing interpretable
AR coefficients and spatiotemporal covariance matrices.
