{"type":"ScholarlyArticle","title":"Full Bayesian Significance Testing for Neural Networks in Traffic Forecasting","authors":[{"name":"Zehua Liu","slug":"zehua-liu","given":"Zehua","family":"Liu","affiliation":"School of Computer Science and Engineering , Beihang University , Beijing , China"},{"name":"Jingyuan Wang","slug":"jingyuan-wang","given":"Jingyuan","family":"Wang","affiliation":"School of Computer Science and Engineering , Beihang University , Beijing , China; School of Economics and Management , Beihang University , Beijing , China; Key Laboratory of Data Intelligence and Management (Beihang University) ,"},{"name":"Zimeng Li","slug":"zimeng-li","given":"Zimeng","family":"Li","affiliation":"School of Computer Science and Engineering , Beihang University , Beijing , China; Ministry of Industry and Information Technology , Beijing , China"},{"name":"Yue He","slug":"yue-he","given":"Yue","family":"He","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing , China"}],"abstract":"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.","doi":"10.24963/ijcai.2024/245","url":"https://ijcai.org/proceedings/2024/245","pdf":"https://ijcai.org/proceedings/2024/0245.pdf","markdown":"https://ijcai.org/proceedings/2024/245.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/245","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":245,"pages":{"first":2216,"last":2224},"keywords":["Machine Learning: ML: Explainable/Interpretable machine learning","Uncertainty in AI: UAI: Uncertainty representations","Knowledge Representation and Reasoning: KRR: Learning and reasoning","Data Mining: DM: Mining spatial and/or temporal data"],"videos":[],"references":[{"position":1,"raw":"[Ahmed and Cook, 1979] Mohammed S Ahmed and Allen R Cook. 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