Uncertainty Quantification for Traffic Forecasting: A Unified Approach
This work addresses uncertainty estimation for traffic forecasting, which is important for urban planning and traffic management, but it is incremental as it builds on existing techniques like variational inference and deep ensembling.
The paper tackles uncertainty quantification in traffic forecasting by developing DeepSTUQ, a method that estimates aleatoric and epistemic uncertainty, and it outperforms state-of-the-art methods on four public datasets in both point prediction and uncertainty quantification.
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.