Full Bayesian Significance Testing for Neural Networks in Traffic Forecasting
Full Bayesian Significance Testing for Neural Networks in Traffic Forecasting
Zehua Liu, Jingyuan Wang, Zimeng Li, Yue He
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 2216-2224.
https://doi.org/10.24963/ijcai.2024/245
Due to the complex and dynamic traffic contexts, the interpretability and uncertainty of traffic forecasting have gained increasing attention. Significance testing is a powerful tool in statistics used to determine whether a hypothesis is valid, facilitating the identification of pivotal features that predominantly contribute to the true relationship. However, existing works mainly regard traffic forecasting as a deterministic problem, making it challenging to perform effective significance testing. To fill this gap, we propose to conduct Full Bayesian Significance Testing for Neural Networks in Traffic Forecasting, namely ST-nFBST. A Bayesian neural network is utilized to capture the complicated traffic relationships through an optimization function resolved in the context of aleatoric uncertainty and epistemic uncertainty. Thereupon, ST-nFBST can achieve the significance testing by means of a delicate grad-based evidence value, further capturing the inherent traffic schema for better spatiotemporal modeling. Extensive experiments are conducted on METR-LA and PEMS-BAY to verify the advantages of our method in terms of uncertainty analysis and significance testing, helping the interpretability and promotion of traffic forecasting.
Keywords:
Data Mining: DM: Mining spatial and/or temporal data
Knowledge Representation and Reasoning: KRR: Learning and reasoning
Uncertainty in AI: UAI: Uncertainty representations
Machine Learning: ML: Explainable/Interpretable machine learning