  Uncertainty is an essential consideration for time series forecasting tasks.
In this work, we specifically focus on quantifying the uncertainty of traffic
forecasting. To achieve this, we develop Deep Spatio-Temporal Uncertainty
Quantification (DeepSTUQ), which can estimate both aleatoric and epistemic
uncertainty. We first leverage a spatio-temporal model to model the complex
spatio-temporal correlations of traffic data. Subsequently, two independent
sub-neural networks maximizing the heterogeneous log-likelihood are developed
to estimate aleatoric uncertainty. For estimating epistemic uncertainty, we
combine the merits of variational inference and deep ensembling by integrating
the Monte Carlo dropout and the Adaptive Weight Averaging re-training methods,
respectively. Finally, we propose a post-processing calibration approach based
on Temperature Scaling, which improves the model's generalization ability to
estimate uncertainty. Extensive experiments are conducted on four public
datasets, and the empirical results suggest that the proposed method
outperforms state-of-the-art methods in terms of both point prediction and
uncertainty quantification.
