Pith. sign in

REVIEW 5 major objections 5 minor 76 references

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction

T0 review · 5 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read UQGNN claims that jointly modeling heterogeneous urban phenomena with a multivariate probabilistic head improves both point-forecast accuracy and uncertainty quantification, reporting 2–5% gains over twelve baselines on four real-world…

desk verdict A workable engineering contribution with a solid evaluation skeleton, but the headline claim is contradicted by the paper's own Table 1 and needs correction before it can be trusted. read the letter →

arxiv 2508.08551 v2 pith:UH3XGTDP submitted 2025-08-12 cs.LG cs.AI

Dahai Yu , Dingyi Zhuang , Lin Jiang , Rongchao Xu , Xinyue Ye , Yuheng Bu , Shenhao Wang , Guang Wang This is my paper · ORCID
classification cs.LGcs.AI
keywords uncertaintyquantificationgraphneuralnetworkspatiotemporalpredictionmultivariateheterogeneousurbanphenomenadiffusionconvolutionprobabilisticforecasting
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that spatiotemporal forecasting improves when the model treats multiple urban phenomena—taxi, bus, subway, bike, and ride flows—as interacting variables and outputs a full predictive distribution rather than a single point estimate. Existing deterministic models give only expected values, while existing probabilistic models usually track one phenomenon and ignore cross-mode correlations. UQGNN couples a multivariate diffusion graph convolution and an interaction-aware temporal convolution to build shared embeddings, then a multivariate probabilistic head predicts a mean vector and covariance matrix. On Shenzhen, NYC, and Chicago datasets the authors report consistent gains of roughly 2–5% in MAE and CRPS over the best of twelve baselines.

What carries the argument

The load-bearing components are the Multivariate Diffusion Graph Convolutional Network and the Interaction-aware Temporal Convolutional Network inside the Interaction-aware Spatiotemporal Embedding module, plus the Multivariate Probabilistic Prediction head. MDGCN replaces single-channel graph diffusion with a cross-layer diffusion convolution that mixes all phenomenon dimensions at each hidden layer; ITCN makes the dilated causal convolution filters sum over all phenomenon channels. The MPP head enforces symmetry and positive definiteness by reconstructing the covariance from its eigenvectors and a clamped eigenvalue matrix, then optimizes the negative log-likelihood $\mathcal{L}=\frac{1}{2}\log|\boldsymbol{\Sigma}|+\frac{1}{2}(\boldsymbol{X}-\boldsymbol{\mu})^\top\boldsymbol{\Sigma}^{-1}(\boldsymbol{X}-\boldsymbol{\mu})$. The covariance is used not only for uncertainty output but to sharpen the mean prediction through the joint loss.

What would settle it

Re-run the experiments with a strictly chronological split—earliest 80% of timestamps for training, next 10% for validation, latest 10% for testing—and compare MAE and CRPS against the same twelve baselines; if UQGNN's margins shrink to noise or reverse, the central superiority claim fails.

Watch

Extended reading notes

Core claim

The central claim is that explicitly modeling correlations among heterogeneous urban phenomena and quantifying their joint uncertainty yields better point forecasts and better-calibrated intervals than modeling each phenomenon separately. In the proposed architecture, the MDGCN applies diffusion convolution across phenomenon layers at every graph node, and the ITCN runs dilated causal convolutions whose filters mix channels from all phenomena; the fused embedding feeds a multivariate probabilistic head that outputs $\boldsymbol{\mu}$ and a positive-definite $\boldsymbol{\Sigma}$, trained by minimizing the multivariate Gaussian negative log-likelihood. The reported result is that UQGNN outperforms twelve baselines on six metrics across four datasets—for example, on Shenzhen an MAE of 9.717 versus 10.053 for the best baseline and a CRPS of 8.399 versus 9.562, which the authors describe as a 5% improvement in both accuracy and uncertainty quantification.

Load-bearing premise

The reported superiority depends on the 8:1:1 train/validation/test split being ordered in time; the paper never states that it is, and with 12-step input windows and a 1-step horizon, an unchronological split could leak future information into training and inflate the 2–5% gains.

Editorial extensions

If this is right

  • Joint prediction lowers error for every individual mode: on Shenzhen each of the five mobility modes improves by 3–7% in MAE and 24–45% in CRPS compared to predicting that mode alone.
  • Removing the multivariate probabilistic head (w/o MPP) or replacing it with separate univariate Gaussians (w/ MPP-) degrades accuracy, so the uncertainty module is part of the point-forecast mechanism, not an add-on.
  • Replacing MDGCN with a standard diffusion GCN degrades Shenzhen MAE from 9.717 to 13.717, indicating that cross-phenomenon spatial mixing carries much of the gain.
  • Selective-regression analysis shows prediction error rises as coverage increases only when uncertainty scores are used, so the estimated uncertainties are informative for abstention decisions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The learned covariance matrix could be read as a data-driven map of mode complementarity (e.g., high bike–subway covariance in the CBD); the paper only shows such correlations via Pearson coefficients, not from the network's output.
  • Because MPP models correlations across phenomena but not across regions or output timesteps, a natural extension is a low-rank or sparse cross-node covariance that would let uncertainty flow between neighborhoods without the $O(N^2)$ blow-up the paper avoids.
  • The per-mode gains are largest in CRPS for sparse phenomena (crime, crash), which suggests the framework may be most valuable in rare-event forecasting settings, a claim the paper does not develop.
  • A strict chronological re-split would settle whether the reported 2–5% edge survives when no test window overlaps a training window; the paper's appendix gives the split ratio but not its ordering.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper proposes UQGNN, a graph neural network framework for multivariate spatiotemporal prediction with uncertainty quantification. The framework has two main components: an Interaction-aware Spatiotemporal Embedding (ISTE) module, containing a Multivariate Diffusion Graph Convolutional Network (MDGCN) and an Interaction-aware Temporal Convolutional Network (ITCN), and a Multivariate Probabilistic Prediction (MPP) module that outputs distribution parameters (mean and covariance for a multivariate Gaussian) and is trained with a negative log-likelihood loss. The authors evaluate UQGNN on four public multivariate datasets from Shenzhen, NYC, and Chicago, comparing against 12 baselines with six metrics (MAE, RMSE, MAPE, KL, MPIW, CRPS). The central claim is that UQGNN consistently outperforms state-of-the-art baselines in both prediction accuracy and uncertainty quantification, with approximately 5% improvements on the Shenzhen dataset. The paper also includes ablations, an analysis of cross-phenomenon interactions, and a comparison of several multivariate distributions.

Significance. If the empirical claims were fully supported, the paper would be a useful contribution: it addresses a real gap by modeling uncertainty jointly across heterogeneous urban phenomena, releases code, evaluates on public datasets against twelve baselines, and provides ablation studies and distribution comparisons. The data-driven motivation in Section 2 is reasonable and clearly presented. However, the significance is currently limited by inconsistencies between the headline claims and the reported numbers, missing statistical rigor, an unspecified temporal split, and ambiguities in the equations that define the two claimed innovations. The conceptual framework and the released code are strengths, but the evidence as presented does not yet substantiate the central claim of consistent superiority.

major comments (5)
  1. [Table 1; Section 5.2; Abstract] The abstract and Section 5.2 state that UQGNN 'consistently outperforms' all baselines and achieves approximately 5% improvements, but Table 1 does not support this. On the Chicago dataset, UQGNN's MAE (1.680) is worse than SUMformer (1.672), its CRPS (0.680) is worse than STGCN (0.664), and its MPIW (2.333) is worse than CF-GNN (2.324). On Shenzhen, the MAE gain over the best baseline DiffSTG is 3.3% (9.717 vs. 10.053), not 5%, while the CRPS gain over STZINB is 12.2% (8.399 vs. 9.562), also not 5%. The headline claim needs to be corrected to a dataset-by-dataset statement or supported by a consistent definition of the reported percentages.
  2. [Tables 1-4; Section 5.1] All reported metrics are single-run point estimates with no error bars, confidence intervals, or significance tests. Since the claimed advantages over the best baselines are only 2-5% on most metrics, run-to-run variance could easily change the ranking; for example, the Chicago MAE gap between UQGNN and SUMformer is 0.008. The authors should report results over multiple random seeds with mean and standard deviation, or provide paired significance tests for the main comparisons.
  3. [Appendix B.3] The data split is described only as 'training, validation, and testing subsets with a ratio of 8:1:1' but it is not stated that the split is chronological. With a 12-step input window and a horizon of 1, a random or shuffled split places test samples whose historical windows overlap training periods, leaking future information and inflating the reported gains. The chronological nature of the split must be stated explicitly, or the experiment must be rerun with a temporal split.
  4. [Section 4.2.1, Eq. (5); Section 4.2.2, Eq. (6)] The two interaction-aware modules are not correctly specified as printed. In Eq. (5), the right-hand side uses H^l_m with the same index m as the left-hand side, and the summation over m=1 to M is therefore vacuous (or yields M copies of the same single-mode diffusion if Theta is shared); no cross-layer coupling between different phenomena appears in the equation. In Eq. (6), the sum over m of f(i) X^m is exactly a standard convolution over the M input channels, so the claimed interaction-aware design reduces to the default multi-channel TCN behavior. The equations need to be rewritten to show the cross-phenomenon mixing, for example by indexing Theta by the source phenomenon or by summing over H^l_{m'} with m' different from m.
  5. [Appendix B.2, Eq. (16); Appendix A.2, Eqs. (10)-(11)] The probabilistic evaluation and the distribution-family claims rely on incorrect formulas. Eq. (16) is not the Kullback-Leibler divergence between two distributions; it is a pointwise ratio and is not even guaranteed to be nonnegative. Eq. (10) is the multivariate Gaussian log-likelihood with beta in place of Sigma, not a multivariate Laplace density. Eq. (11) adds a negative-binomial marginals term to a Gaussian quadratic form, which is not a proper joint multivariate negative binomial likelihood. Since RQ5 and the KL column in Tables 1-4 depend on these definitions, the formulas must be corrected and the affected experiments re-run.
minor comments (5)
  1. [Figure 4] The architecture diagram labels the framework 'UGQNN'; the acronym should be UQGNN consistently.
  2. [Section 5.1.2; Appendix B.1.2] The baseline CF-GNN is referred to as 'CF-CNN' in these sections; please use one name consistently throughout the paper.
  3. [Appendix B.2] The abbreviation 'CPRS' appears in the CRPS definition; this should be 'CRPS'.
  4. [Section 4.3] The text states that diagonal entries of the covariance matrix capture 'uncertainties caused by their interactions'; interactions between different phenomena are encoded by the off-diagonal entries, not the diagonal entries.
  5. [Section 5.2] The phrase 'almost all metrics' should be quantified; Table 1 shows that UQGNN is not the best on several Chicago metrics (MAE, CRPS, MPIW).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the claimed results rest on an external benchmark comparison with a learned probabilistic head, not on a self-referential derivation.

full rationale

UQGNN's central claims are empirical. The model is trained end-to-end by minimizing the multivariate Gaussian negative log-likelihood in Eq. (9), and the mean and covariance are neural-network outputs learned from data, not hand-set constants and not re-statements of the evaluation metrics. The evaluation metrics in Eqs. (12)-(17) are standard and external to the training loss, so the reported MAE/CRPS improvements are not implied by construction. The architectural components (MDGCN in Eq. (5), ITCN in Eq. (6), Hadamard fusion, and the covariance reconstruction in Algorithm 1) are explicit modeling choices, and the ablations in Table 2 compare the full model against concrete variants, so component contributions are asserted experimentally rather than by definition. The paper does cite the authors' own prior STZINB work [70] and SAUC [69], but only as baseline and related work; the comparison against STZINB is a quantitative external benchmark on public datasets, so those self-citations are not load-bearing. No uniqueness theorem or prior-work ansatz is imported to force the model choice; the multivariate Gaussian assumption is stated directly in Section 4.3. Concerns that the 8:1:1 split in Appendix B.3 may not be chronological, and that Table 1 appears to contradict the abstract's '5%' and 'consistently outperforms' wording, are validity and correctness questions rather than circular derivations, because no equation or construction in the paper reduces the reported output to a fitted input or to the paper's own prior conclusions. Accordingly, no circular step can be exhibited under the required standard.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The model introduces no new physical entities or ad hoc constants beyond standard hyperparameters. The two hand-chosen controls (graph kernel thresholds and the eigenvalue clamp) and the Gaussian distributional assumption are the main premises. Learned neural network weights are trained on the target data and are not listed as free parameters.

free parameters (2)
  • Adjacency kernel thresholds sigma^2 and r = Not reported
    Equation 1 defines the graph via exp(-d^2/sigma^2) with threshold r; these controls are hand-chosen, determine sparsity and connectivity, and affect all subsequent diffusion convolutions. Appendix A.1 does not give their values.
  • Eigenvalue clamp V_min = Range 1e-6 to 1e-2 reported
    Algorithm 1 clamps covariance eigenvalues to V_min to ensure positive definiteness. The paper states that this value must be tuned and that it affects training and accuracy.
assumptions (4)
  • domain assumption Heterogeneous urban phenomena at a region follow a multivariate Gaussian distribution.
    Stated in Section 4.3: the authors assume heterogeneous urban phenomena follow a multivariate Gaussian distribution. This is used in the NLL loss and in interpreting Sigma as uncertainty, including the MPIW computation as 1.96 times sigma.
  • domain assumption Spatial proximity, encoded by a Gaussian kernel of centroid distances, captures the interaction strength between regions.
    Equation 1 assumes edge weight decays with distance. All spatial diffusion results depend on this graph construction.
  • domain assumption The 8:1:1 train/validation/test split is temporally ordered so that no test sample's 12-step input history overlaps training data.
    Appendix B.3 gives only the split ratio. The validity of the benchmark comparison assumes no temporal leakage.
  • standard math The random walk with restart diffusion process converges to the stationary distribution given in closed form.
    Equation 4 is the standard random-walk-with-restart stationary distribution; accepted background for diffusion convolution.

how reviews work

0 comments
Cite this review

Pith. "Pith review of UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction." pith.science (2026). https://pith.science/paper/UH3XGTDP

@misc{pith2026250808551,
  author       = {Pith},
  title        = {Pith review of: UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UH3XGTDP}},
  note         = {Machine review of arXiv:2508.08551}
}
read the original abstract

Spatiotemporal prediction plays a critical role in numerous real-world applications such as urban planning, transportation optimization, disaster response, and pandemic control. In recent years, researchers have made significant progress by developing advanced deep learning models for spatiotemporal prediction. However, most existing models are deterministic, i.e., predicting only the expected mean values without quantifying uncertainty, leading to potentially unreliable and inaccurate outcomes. While recent studies have introduced probabilistic models to quantify uncertainty, they typically focus on a single phenomenon (e.g., taxi, bike, crime, or traffic crashes), thereby neglecting the inherent correlations among heterogeneous urban phenomena. To address the research gap, we propose a novel Graph Neural Network with Uncertainty Quantification, termed UQGNN for multivariate spatiotemporal prediction. UQGNN introduces two key innovations: (i) an Interaction-aware Spatiotemporal Embedding Module that integrates a multivariate diffusion graph convolutional network and an interaction-aware temporal convolutional network to effectively capture complex spatial and temporal interaction patterns, and (ii) a multivariate probabilistic prediction module designed to estimate both expected mean values and associated uncertainties. Extensive experiments on four real-world multivariate spatiotemporal datasets from Shenzhen, New York City, and Chicago demonstrate that UQGNN consistently outperforms state-of-the-art baselines in both prediction accuracy and uncertainty quantification. For example, on the Shenzhen dataset, UQGNN achieves a 5% improvement in both prediction accuracy and uncertainty quantification.

Figures

Figures reproduced from arXiv: 2508.08551 by the authors.

Figure 1
Figure 1. Dominant mobility mode in different regions at different hours in Shenzhen. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Five urban phenomena in different regions. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Correlations of five urban phenomena (mobility [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: An overall framework of UQGNN. It consists of an Interaction-aware Spatiotemporal Embedding Module that includes [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Prediction results of the taxi flow on the NYC Dataset 1 in terms of the last 96 timesteps, i.e., 24 hours, with best [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Selective regression for our UQGNN. 5.4 Ablation Study (RQ 3) We conduct an ablation study to show the effectiveness of each key component in UQGNN. Specifically, we compare UQGNN with four variants by removing a component in UQGNN. The four variants are explained belo…
Figure 7
Figure 7. Figure 7: Prediction error of each urban phenomenon. [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

76 extracted references · 51 canonical work pages

  1. [1]

    James Atwood and Don Towsley. 2016. Diffusion-convolutional neural networks. Advances in neural information processing systems 29 (2016)

    work page 2016

  2. [2]

    Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems 33 (2020), 17804–17815

    2020

  3. [3]

    Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, et al. 2020. Spectral temporal graph neural network for multivariate time-series forecasting. Advances in neural information processing systems 33 (2020), 17766–17778

    2020

  4. [4]

    Xiwen Chen, Sayed Pedram Haeri Boroujeni, Xin Shu, Huayu Li, and Abolfazl Razi. 2024. Enhancing Graph Neural Networks in Large-scale Traffic Incident Analysis with Concurrency Hypothesis. In Proceedings of the 32nd ACM Interna- tional Conference on Advances in Geographic Information Systems . 196–207

    work page 2024

  5. [5]

    Jinguo Cheng, Ke Li, Yuxuan Liang, Lijun Sun, Junchi Yan, and Yuankai Wu. 2025. Rethinking Urban Mobility Prediction: A Multivariate Time Series Forecasting Approach. IEEE Transactions on Intelligent Transportation Systems (2025)

    work page 2025

  6. [6]

    Bikesharing

    Chicago. Bikesharing. https://data.cityofchicago.org/Transportation/Divvy- Trips/fg6s-gzvg/about_data

    work page

  7. [7]

    Chicago. Crime. https://data.cityofchicago.org/Public-Safety/Crimes-2022/9hwr- 2zxp/data

    work page 2022

  8. [8]

    Chicago. Taxi. https://data.cityofchicago.org/Transportation/Taxi-Trips-2013- 2023-/wrvz-psew/about_data

    work page 2013

Show all 76 references
  1. [9]

    Israel Cohen, Yiteng Huang, Jingdong Chen, Jacob Benesty, Jacob Benesty, Jing- dong Chen, Yiteng Huang, and Israel Cohen. 2009. Pearson correlation coefficient. Noise reduction in speech processing (2009), 1–4

    2009

  2. [10]

    Jiewen Deng, Renhe Jiang, Jiaqi Zhang, and Xuan Song. 2024. Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning. arXiv preprint arXiv:2405.03255 (2024)

    2024 arXiv

  3. [11]

    Ziquan Fang, Lu Pan, Lu Chen, Yuntao Du, and Yunjun Gao. 2021. MDTP: A multi-source deep traffic prediction framework over spatio-temporal trajectory data. Proceedings of the VLDB Endowment 14, 8 (2021), 1289–1297

    2021

  4. [12]

    Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. 2018. Deepmove: Predicting human mobility with attentional recurrent networks. In Proceedings of the 2018 world wide web conference . 1459–1468

    2018

  5. [13]

    Jie Feng, Zeyu Yang, Fengli Xu, Haisu Yu, Mudan Wang, and Yong Li. 2020. Learning to simulate human mobility. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining . 3426–3433

    2020

  6. [14]

    Spencer J Fox, Michael Lachmann, Mauricio Tec, Remy Pasco, Spencer Woody, Zhanwei Du, Xutong Wang, Tanvi A Ingle, Emily Javan, Maytal Dahan, et al

  7. [15]

    Yang Guo, Zhiqi Wang, Jin Xue, and Zili Shao. 2024. A spatio-temporal series data model with efficient indexing and layout for cloud-based trajectory data management. In 2024 IEEE 40th International Conference on Data Engineering (ICDE). IEEE, 1171–1184

    2024

  8. [16]

    Jindong Han, Weijia Zhang, Hao Liu, Tao Tao, Naiqiang Tan, and Hui Xiong. 2024. BigST: Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road Networks. Proceedings of the VLDB Endowment 17, 5 (2024), 1081–1090

    2024

  9. [17]

    Wenchong He and Zhe Jiang. 2023. A Survey on Uncertainty Quantification Methods for Deep Neural Networks: An Uncertainty Source Perspective. arXiv preprint arXiv:2302.13425 (2023)

    2023

  10. [18]

    Junfeng Hu, Xu Liu, Zhencheng Fan, Yuxuan Liang, and Roger Zimmermann

  11. [19]

    Kexin Huang, Ying Jin, Emmanuel Candes, and Jure Leskovec. 2024. Uncertainty quantification over graph with conformalized graph neural networks. Advances in Neural Information Processing Systems 36 (2024)

    2024

  12. [20]

    Lin Jiang, Yu Yang, and Guang Wang. 2025. HCRide: Harmonizing Passenger Fairness and Driver Preference for Human-Centered Ride-Hailing. In IJCAI

    2025

  13. [21]

    Lin Jiang, Dahai Yu, Rongchao Xu, Tian Tang, and Guang Wang. 2025. Uncertainty-aware Predict-Then-Optimize Framework for Equitable Post- Disaster Power Restoration. In IJCAI

    2025

  14. [22]

    Renhe Jiang, Xuan Song, Zipei Fan, Tianqi Xia, Quanjun Chen, Satoshi Miyazawa, and Ryosuke Shibasaki. 2018. Deepurbanmomentum: An online deep-learning system for short-term urban mobility prediction. In Proceedings of the AAAI conference on artificial intelligence , Vol. 32

    2018

  15. [23]

    Xiyuan Jin, Jing Wang, Shengnan Guo, Tonglong Wei, Yiji Zhao, Youfang Lin, and Huaiyu Wan. 2024. Spatial–temporal uncertainty-aware graph networks for promoting accuracy and reliability of traffic forecasting. Expert Systems with Applications 238 (2024), 122143

    2024

  16. [24]

    Jian Kang, Yan Zhu, Yinglong Xia, Jiebo Luo, and Hanghang Tong. 2022. Rawls- gcn: Towards rawlsian difference principle on graph convolutional network. In Proceedings of the ACM Web Conference 2022 . 1214–1225

    2022

  17. [25]

    Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, and Pyang Li. 2022. DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting. InProceedings of the 39th International Con- ference on Machine Learning (Proceedings of Machine Learn...

    2022

  18. [26]

    Colin Lea, Michael D Flynn, Rene Vidal, Austin Reiter, and Gregory D Hager

  19. [27]

    Colin Lea, Rene Vidal, Austin Reiter, and Gregory D Hager. 2016. Temporal convolutional networks: A unified approach to action segmentation. InComputer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14 . Springer, 47–54

    2016

  20. [28]

    Huan Li, Lanjing Yi, Bo Tang, Hua Lu, and Christian S Jensen. 2022. Efficient and error-bounded spatiotemporal quantile monitoring in edge computing envi- ronments. Proceedings of the VLDB Endowment 15, 9 (2022), 1753–1765

    2022

  21. [29]

    Shuhao Li, Yue Cui, Libin Li, Weidong Yang, Fan Zhang, and Xiaofang Zhou. 2024. ST-ABC: Spatio-Temporal Attention-Based Convolutional Network for Multi- Scale Lane-Level Traffic Prediction. In 2024 IEEE 40th International Conference on Data Engineering (ICDE). IEEE, 1185–1198

    2024

  22. [30]

    Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2017. Diffusion convolu- tional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)

    2017 arXiv

  23. [31]

    Zhonghang Li, Chao Huang, Lianghao Xia, Yong Xu, and Jian Pei. 2022. Spatial- temporal hypergraph self-supervised learning for crime prediction. In 2022 IEEE 38th international conference on data engineering (ICDE) . IEEE, 2984–2996

    2022

  24. [32]

    Yuebing Liang, Guan Huang, and Zhan Zhao. 2022. Joint demand prediction for multimodal systems: A multi-task multi-relational spatiotemporal graph neural network approach. Transportation Research Part C: Emerging Technologies 140 (2022), 103731

    2022

  25. [33]

    Zhihui Lin, Maomao Li, Zhuobin Zheng, Yangyang Cheng, and Chun Yuan. 2020. Self-attention convlstm for spatiotemporal prediction. In Proceedings of the AAAI conference on artificial intelligence , Vol. 34. 11531–11538

    2020

  26. [34]

    Hao Liu, Jindong Han, Yanjie Fu, Jingbo Zhou, Xinjiang Lu, and Hui Xiong. 2020. Multi-modal transportation recommendation with unified route representation learning. Proceedings of the VLDB Endowment 14, 3 (2020), 342–350

    2020

  27. [35]

    Kaiqi Liu, Panrong Tong, Mo Li, Yue Wu, and Jianqiang Huang. 2023. ST4ML: Machine learning oriented spatio-temporal data processing at scale. Proceedings of the ACM on Management of Data 1, 1 (2023), 1–28

    2023

  28. [36]

    Mingzhe Liu, Bowen Du, and Leilei Sun. 2021. Co-prediction of multimodal transportation demands with self-learned spatial dependence. In 2021 IEEE Inter- national Conference on Big Data (Big Data) . IEEE, 824–833

    2021

  29. [37]

    Yuyu Luo, Wenbo Li, Tianyu Zhao, Xiang Yu, Lixi Zhang, Guoliang Li, and Nan Tang. 2020. Deeptrack: Monitoring and exploring spatio-temporal data: A case of tracking COVID-19. Proceedings of the VLDB Endowment 13, 12 (2020), 2841–2844

    2020

  30. [38]

    Bikesharing

    NYC. Bikesharing. https://citibikenyc.com/system-data

  31. [39]

    NYC. Crash. https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle- Collisions-Crashes/h9gi-nx95/about_data

  32. [40]

    NYC. Crime. https://data.cityofnewyork.us/Public-Safety/NYC-crime/qb7u- rbmr/about_data

  33. [41]

    NYC. Subway. https://www.mta.info/article/introducing-subway-origin- destination-ridership-dataset

  34. [42]

    NYC. Taxi. https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page

  35. [43]

    Weizhu Qian, Dalin Zhang, Yan Zhao, Kai Zheng, and JQ James. 2023. Uncer- tainty quantification for traffic forecasting: A unified approach. In 2023 IEEE 39th International Conference on Data Engineering (ICDE) . IEEE, 992–1004

    2023

  36. [44]

    Jensen, Zhenli Sheng, and Bin Yang

    Xiangfei Qiu, Jilin Hu, Lekui Zhou, Xingjian Wu, Junyang Du, Buang Zhang, Chenjuan Guo, Aoying Zhou, Christian S. Jensen, Zhenli Sheng, and Bin Yang

  37. [45]

    Kashif Rasul, Calvin Seward, Ingmar Schuster, and Roland Vollgraf. 2021. Au- toregressive denoising diffusion models for multivariate probabilistic time series forecasting. In International Conference on Machine Learning . PMLR, 8857–8868

    2021

  38. [46]

    Keven Richly, Rainer Schlosser, and Martin Boissier. 2022. Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications. Pro- ceedings of the VLDB Endowment 15, 13 (2022), 4079–4092

    2022

  39. [47]

    Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, and Xavier Bresson

  40. [48]

    TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods. Proc. VLDB Endow. 17, 9 (2024), 2363–2377

    2024

  41. [49]

    Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, and Chris- tian S Jensen. 2022. Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. arXiv preprint arXiv:2206.09112 (2022)

    2022 arXiv

  42. [50]

    Dingyuan Shi, Bingchen Song, Yuanyuan Zhang, Haolong Yang, and Ke Xu. 2024. A Data-driven Spatiotemporal Simulator for Reinforcement Learning Methods. Proceedings of the VLDB Endowment 17, 12 (2024), 4257–4260

    2024

  43. [51]

    Anna Sokol, Nuno Moniz, and Nitesh Chawla. 2024. Conformalized Selective Regression. arXiv preprint arXiv:2402.16300 (2024)

    2024 arXiv

  44. [52]

    Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, and Ryosuke Shibasaki. 2014. Prediction of human emergency behavior and their mobility following large- scale disaster. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . 5–14

    2014

  45. [53]

    Abhin Shah, Yuheng Bu, Joshua K Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, and Gregory W Wornell. 2022. Selective regression under fairness criteria. In International Conference on Machine Learning . PMLR, 19598–19615. SIGSPATIAL ’25, November 3–6, 2025, Minneapolis,...

    2022

  46. [54]

    Libin Wang, Raymond Chi-Wing Wong, and Christian S Jensen. 2024. Congestion- Mitigating Spatiotemporal Routing in Road Networks. In 2024 IEEE 40th Interna- tional Conference on Data Engineering (ICDE) . IEEE, 4586–4599

    2024

  47. [55]

    Yuandong Wang, Hongzhi Yin, Hongxu Chen, Tianyu Wo, Jie Xu, and Kai Zheng

  48. [56]

    Haomin Wen, Youfang Lin, Yutong Xia, Huaiyu Wan, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang. 2023. Diffstg: Probabilistic spatio-temporal graph forecasting with denoising diffusion models. InProceedings of the 31st ACM International Conference on Advances in Geographic I...

    2023

  49. [57]

    Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)

    2019 arXiv

  50. [58]

    Jiachuan Wang, Peng Cheng, Libin Zheng, Chao Feng, Lei Chen, Xuemin Lin, and Zheng Wang. 2020. Demand-aware route planning for shared mobility services. Proceedings of the VLDB Endowment 13, 7 (2020), 979–991

    2020

  51. [59]

    Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, Xinran Tong, and Hui Xiong. 2019. Co-prediction of multiple transportation demands based on deep spatio-temporal neural network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining . 305–313

    2019

  52. [60]

    Xinyue Ye, Tan Yigitcanlar, Yangyang Xu, Monique Head, Yun Hang, Tom Sanchez, Wenjing Gong, and Dev Niyogi. 2025. Urban planning and IPCC- like city assessments integration for climate-resilient cities. Environment and Planning B: Urban Analytics and City Science (2025), 23998...

    2025

  53. [61]

    Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph con- volutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)

    2017 arXiv

  54. [62]

    Haitao Yuan, Guoliang Li, and Zhifeng Bao. 2022. Route travel time estimation on a road network revisited: Heterogeneity, proximity, periodicity and dynamicity. Proceedings of the VLDB Endowment 16, 3 (2022), 393–405

    2022

  55. [63]

    Xiaoqi Zhang, Zheng Ji, Yanqiao Zheng, Xinyue Ye, and Dong Li. 2020. Evaluating the effect of city lock-down on controlling COVID-19 propagation through deep learning and network science models. Cities 107 (2020), 102869

    2020

  56. [64]

    Yuanyuan Yao, Dimeng Li, Hailiang Jie, Hailiang Jie, Tianyi Li, Jie Chen, Jiaqi Wang, Feifei Li, and Yunjun Gao. 2023. SimpleTS: An Efficient and Universal Model Selection Framework for Time Series Forecasting. Proc. VLDB Endow. 16, 12 (Aug. 2023), 3741–3753. doi:10.14778/3611...

    2023

  57. [65]

    Jie Zhao, Chao Chen, Wanyi Zhang, Ruiyuan Li, Fuqiang Gu, Songtao Guo, Jun Luo, and Yu Zheng. 2023. Coupling Makes Better: An Intertwined Neural Network for Taxi and Ridesourcing Demand Co-Prediction.IEEE Transactions on Intelligent Transportation Systems (2023)

    2023

  58. [66]

    Kai Zhao, Chenjuan Guo, Yunyao Cheng, Peng Han, Miao Zhang, and Bin Yang

  59. [67]

    Zhengyang Zhou, Yang Wang, Xike Xie, Lei Qiao, and Yuantao Li. 2021. STU- aNet: Understanding Uncertainty in Spatiotemporal Collective Human Mobil- ity. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21). Association for Computing Machinery, New York, NY...

    2021

  60. [68]

    Zhengyang Zhou, Kuo Yang, Yuxuan Liang, Binwu Wang, Hongyang Chen, and Yang Wang. 2023. Predicting collective human mobility via countering spatiotemporal heterogeneity. IEEE Transactions on Mobile Computing (2023)

    2023

  61. [69]

    Dingyi Zhuang, Yuheng Bu, Guang Wang, Shenhao Wang, and Jinhua Zhao. 2024. SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks. In Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Syste...

    2024

  62. [70]

    Zijun Zhang. 2018. Improved adam optimizer for deep neural networks. In 2018 IEEE/ACM 26th international symposium on quality of service (IWQoS) . Ieee, 1–2

    2018

  63. [77]

    Dingyi Zhuang, Shenhao Wang, Haris Koutsopoulos, and Jinhua Zhao. 2022. Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial- Temporal Graph Neural Networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washingt...

    2022

  64. [2017]

    In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

    Temporal convolutional networks for action segmentation and detection. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 156–165

  65. [2018]

    In Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I 25

    Structured sequence modeling with graph convolutional recurrent net- works. In Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I 25 . Springer, 362–373

    2018

  66. [2019]

    In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining

    Origin-destination matrix prediction via graph convolution: a new per- spective of passenger demand modeling. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining . 1227–1235

  67. [2022]

    Proceedings of the National Academy of Sciences 119, 7 (2022), e2111870119

    Real-time pandemic surveillance using hospital admissions and mobility data. Proceedings of the National Academy of Sciences 119, 7 (2022), e2111870119

    2022

  68. [2023]

    Proceedings of the VLDB Endowment 17, 4 (2023), 753–765

    Multiple time series forecasting with dynamic graph modeling. Proceedings of the VLDB Endowment 17, 4 (2023), 753–765

    2023

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.