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SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks

13 Sep 2024arXiv:2409.08766archive 2025-07-28

Dingyi Zhuang, Yuheng Bu, Guang Wang, Shenhao Wang, Jinhua Zhao

Quantifying uncertainty is crucial for robust and reliable predictions. However, existing spatiotemporal deep learning mostly focuses on deterministic prediction, overlooking the inherent uncertainty in such prediction. Particularly, highly-granular spatiotemporal datasets are often sparse, posing extra challenges in prediction and uncertainty quantification. To address these issues, this paper introduces a novel post-hoc Sparsity-awar Uncertainty Calibration (SAUC) framework, which calibrates uncertainty in both zero and non-zero values. To develop SAUC, we firstly modify the state-of-the-art deterministic spatiotemporal Graph Neural Networks (ST-GNNs) to probabilistic ones in the pre-calibration phase. Then we calibrate the probabilistic ST-GNNs for zero and non-zero values using quantile approaches.Through extensive experiments, we demonstrate that SAUC can effectively fit the variance of sparse data and generalize across two real-world spatiotemporal datasets at various granularities. Specifically, our empirical experiments show a 20\% reduction in calibration errors in zero entries on the sparse traffic accident and urban crime prediction. Overall, this work demonstrates the theoretical and empirical values of the SAUC framework, thus bridging a significant gap between uncertainty quantification and spatiotemporal prediction.

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2ran · violated contract
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accuracy_numerical AnonymousSAUC/SAUC/utils.py official repository ran fingerprinted no licence file found · pointer only · 96a81bc63aa914b9 · report
accuracy_softmax AnonymousSAUC/SAUC/utils.py official repository ran no licence file found · pointer only · cd46e2daab25968b · report
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calculate_normalized_laplacian AnonymousSAUC/SAUC/models/GWN/util.py official repository ran · our draft was wrong no licence file found · pointer only · e7b2e6a60daa42ca · report
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calibrate_model AnonymousSAUC/SAUC/sauc.py official repository unverified no licence file found · pointer only · a9a2b2572f105cf8 · report
generate_graph_seq2seq_io_data AnonymousSAUC/SAUC/models/GWN/generate_training_data.py official repository unverified no licence file found · pointer only · 2635b8fbfe12d9ff · report
load_crash_data AnonymousSAUC/SAUC/models/STGCN/utils.py official repository unverified no licence file found · pointer only · 1bdadc43945d3430 · report
load_crime_data AnonymousSAUC/SAUC/models/STGCN/utils.py official repository unverified no licence file found · pointer only · be13982b384bdef3 · report
load_demand_data AnonymousSAUC/SAUC/models/STGCN/utils.py official repository unverified no licence file found · pointer only · e5732012d322d739 · report

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Crime PredictionPredictionUncertainty Quantification

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