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REVIEW 3 major objections 5 minor 5 cited by

Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The case for one spatio-temporal foundation model that generalizes across domains, space, time, and scale.

desk verdict Useful, honest vision paper whose central premise—cross-domain transfer—remains unproven; the qualitative table could be more rigorous. read the letter →

arxiv 2501.09045 v2 pith:3NK5OPLA submitted 2025-01-15 cs.CV cs.AIcs.ET

Adam Goodge , Wee Siong Ng , Bryan Hooi , See Kiong Ng This is my paper · ORCID
classification cs.CVcs.AIcs.ET
keywords spatio-temporalfoundationmodelsgeneralizationdomainspatialtemporalscalesurvey
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 argues that spatio-temporal data—traffic flows, weather fields, disease spread—can and should be served by a spatio-temporal foundation model (STFM): a large pretrained network that learns universal patterns of spatial and temporal dependence and transfers across many tasks. It defines an STFM by four generalization capabilities: across domains (traffic vs. weather vs. public health), across locations, across time periods, and across spatial and temporal scales. Surveying six current STFMs, the paper finds none that meets all four: transportation models stay in cities, weather models stay at coarse global grids, and temporal dependencies are often sidestepped. The payoff of getting this right would be shared training across data-rich and data-poor applications, faster adaptation to new cities or new prediction horizons, and better performance in domains with little data.

What carries the argument

The central object is the four-way generalization framework (domain, spatial, temporal, scale), together with the distinction between in-distribution and out-of-distribution generalization. This framework is what turns the broad Definition 3.2 into an evaluable research agenda: each existing STFM is assessed against the four capabilities, and the identified gaps motivate the paper's proposed directions. The framework also carries the argument that 'foundation-ness' is a spectrum, not a binary.

What would settle it

A controlled large-scale experiment would settle it: pretrain one STFM on a diverse mix of datasets (e.g., traffic flow, weather variables, and disease incidence) and compare its performance on held-out tasks in each domain against a single-domain model trained only on that domain's data. If the STFM does not beat or match the single-domain baselines on the majority of tasks—indicating negative transfer—the paper's central premise is refuted.

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Extended reading notes

Core claim

The paper's central claim is Definition 3.2: a spatio-temporal foundation model is a large-scale neural network pretrained on diverse spatio-temporal data sources, designed to generalize across multiple tasks by learning universal patterns of spatial and temporal dependencies. Generalization here comes in four forms—domain, spatial, temporal, and scale—and can be tested either in-distribution (on tasks seen during pretraining) or out-of-distribution (on truly new tasks). The paper's key finding is that current STFMs are fragmented: UniST, OpenCity, and UrbanGPT focus on urban transportation, while ClimaX, Pangu-Weather, and W-MAE focus on global weather; no study fully addresses the full range of spatio-temporal applications. It therefore calls for unified architectures, cross-domain synergies, multi-modal training, and adaptation to distribution shift.

Load-bearing premise

The load-bearing premise is that heterogeneous spatio-temporal domains share enough common structure that training one large model on many of them transfers positively—if that transfer fails, the universal STFM loses its foundation and the field should stick to single-domain models.

Editorial extensions

If this is right

  • If STFMs succeed, a single pretrained model could serve prediction, classification, anomaly detection, and imputation across cities and applications without per-task retraining.
  • Existing transportation STFMs are limited by data from a few US and Chinese cities, so broader geographic coverage is needed for true spatial generalization.
  • Weather STFMs that use only one timestamp of input do not explicitly model temporal dependencies, so they cannot yet count as full STFMs under the paper's definition.
  • Standardized benchmarks with consistent horizons, resolutions, and ID/OOD protocols are necessary to compare STFMs and drive progress.
  • Incorporating directional cross-domain knowledge (e.g., weather drives traffic, not vice versa) and causality could unlock synergies between domains.

Reading between the lines

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

  • The four generalization axes suggest a concrete test suite: any proposed STFM should report results on at least one held-out domain, one unseen city or region, one future time period, and one new resolution; current papers rarely do all four.
  • The negative-transfer concern is the sharpest threat: if cross-domain pretraining fails between, say, traffic incidents and disease outbreaks, the universal STFM vision may reduce to a portfolio of domain-specific models, and the paper's own uncertainty about this should be treated as an open empirical question.
  • Multi-modal inputs (satellite imagery, maps, text) may be the cheapest route to shared structure, since maps and physical constraints already encode relationships across domains, rather than relying on the model to discover them from raw numbers.
  • The framework could be extended to other data-rich but model-poor settings, such as ecology or oceanography, where the same four generalizations apply and benchmark standardization would accelerate progress.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. This position paper articulates a vision for spatio-temporal foundation models (STFMs). It defines an STFM as a large-scale neural network pre-trained on diverse spatio-temporal data sources and designed to generalize across multiple tasks by learning universal patterns of spatial and temporal dependencies (Definition 3.2). The paper identifies four forms of generalization—domain, spatial, temporal, and scale—and uses them to assess six recent models: UniST, OpenCity, UrbanGPT, ClimaX, Pangu-Weather, and W-MAE. It concludes that current research is fragmented into transportation and weather silos and that no existing study comprehensively addresses the full range of spatio-temporal applications. The final section outlines opportunities in unified architectures, cross-domain synergies, multi-modal training, and adaptation to distribution shift.

Significance. The paper provides a useful synthesis and taxonomy for a rapidly growing but scattered research area. Its explicit acknowledgement that Table I is qualitative and not a performance comparison is a sign of scholarly care. The four-way generalization decomposition and the critical survey of six STFMs will likely be valuable to researchers entering the field. The main contribution is conceptual rather than empirical, and its influence will depend on whether the community adopts the proposed definition and benchmark agenda. However, the central definition rests on an empirically unverified positive-transfer premise, which the paper itself acknowledges; this limits the certainty with which current models can be judged against the proposed ideal.

major comments (3)
  1. [Section III-A1 and Definition 3.2] Definition 3.2 asserts that an STFM learns 'universal patterns of spatial and temporal dependencies' from 'diverse spatio-temporal data sources.' This presupposes that beneficial shared patterns exist across heterogeneous application domains. Yet Section III-A1 explicitly concedes that for disparate applications such as traffic incidents and disease outbreaks, 'it is uncertain whether training STFMs to simultaneously model both applications would enhance or degrade performance for either.' This tension is load-bearing because the entire gap analysis in Section IV evaluates existing models against a definition that presumes a positive answer to this open empirical question. The authors should reframe Definition 3.2 as a research hypothesis or aspirational goal rather than an established property, and should discuss what evidence would falsify the premise.
  2. [Section IV, Table I] The three-level symbol ratings in Table I are presented as an assessment of generalization capabilities, but no transparent scoring rubric is provided. The text lists two factors (data suitability for pre-training and experimental demonstration) and then asserts a mapping to positive/neutral/negative symbols. Because the paper's central diagnosis—that current STFMs are fragmented and fall short of the proposed ideal—relies substantially on this qualitative table, the assessment is not independently checkable. I recommend that the authors include a concrete rubric in an appendix, define thresholds for each symbol, or clearly label the table as an editorial judgment with a caveat about its subjectivity.
  3. [Section IV-4 (Scale Generalization)] The statement that Pangu-Weather's use of four separate lead-time models is 'fundamentally at odds with the intended purpose of a foundation model' conflates the proposed STFM ideal with a requirement for a single unified set of weights. Foundation models in language and vision routinely use task-specific heads, adapters, and even per-task fine-tuning while still satisfying the broader definition of pre-training on diverse data followed by transfer. This normative claim should be softened or justified relative to Definition 3.2, which does not explicitly prohibit architecture or training-time decomposition by downstream setting.
minor comments (5)
  1. [Section II-A] The sentence 'With this, the data structure is represented as a four dimensional matrix' should read 'four-dimensional tensor,' since the object X ∈ R^{C×L×H×W} has four modes, not a matrix in the usual sense.
  2. [Section IV-1] There is a typo: 'perdiction performance for only 4 of them' should be 'prediction performance.'
  3. [Section IV Overview] The word 'incorporiate' in 'Weather foundation models often incorporiate a wide variety' should be 'incorporate.'
  4. [Section IV-2] The phrase 'the local minmum of mean sea level pressure' contains a typo: 'minmum' should be 'minimum.'
  5. [Figure 2] Figure 2, which illustrates the proposed STFM framework, is never referenced in the body text. The authors should add an explicit callout in Section III or elsewhere so that readers know when to consult it.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a vision and survey document with no derivation chain, fitted parameters, or load-bearing self-citations.

full rationale

This is a position paper rather than a technical derivation. It defines a spatio-temporal foundation model (Definition 3.2) in broad terms, enumerates desirable generalization capabilities, surveys six existing models, and identifies research gaps and opportunities. There is no equation whose output is equivalent to its input, no parameter fitted to a dataset and then renamed as a prediction, and no invoked uniqueness theorem from the authors' prior work. The paper's central claim is normative and conditional: if shared spatio-temporal patterns exist across domains, then STFMs are a promising direction. The paper itself explicitly acknowledges the main uncertainty in Section III-A1, writing that for disparate applications such as traffic incidents and disease outbreaks, 'it is uncertain whether training STFMs to simultaneously model both applications would enhance or degrade performance for either.' This is an honest limitation rather than a circular step. The assessment of existing models against the authors' own definition is a framework choice, not a self-consistency proof; it may be debatable, but it does not reduce to a prior conclusion by construction. No self-citations are load-bearing, and the surveyed models are externally reported works. The paper is self-contained as a vision/survey and does not claim to derive empirical results from its own assumptions.

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

The paper introduces no fitted parameters and no new physical entities. Its conceptual axioms are the existence of transferable ST patterns and the normative definition of an STFM.

assumptions (2)
  • domain assumption Shared patterns exist across diverse spatio-temporal domains and can be learned jointly.
    Invoked in Section III-A1 as the basis for cross-domain STFMs; the authors themselves flag uncertainty about negative transfer.
  • domain assumption Heterogeneity and autocorrelation are the two defining properties of all spatio-temporal data.
    Taken from the cited survey [26] in Section II-A and used to motivate why ST data breaks i.i.d. assumptions.

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Cite this review

Pith. "Pith review of Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities." pith.science (2026). https://pith.science/paper/3NK5OPLA

@misc{pith2026250109045,
  author       = {Pith},
  title        = {Pith review of: Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3NK5OPLA}},
  note         = {Machine review of arXiv:2501.09045}
}
read the original abstract

Foundation models have revolutionized artificial intelligence, setting new benchmarks in performance and enabling transformative capabilities across a wide range of vision and language tasks. However, despite the prevalence of spatio-temporal data in critical domains such as transportation, public health, and environmental monitoring, spatio-temporal foundation models (STFMs) have not yet achieved comparable success. In this paper, we articulate a vision for the future of STFMs, outlining their essential characteristics and the generalization capabilities necessary for broad applicability. We critically assess the current state of research, identifying gaps relative to these ideal traits, and highlight key challenges that impede their progress. Finally, we explore potential opportunities and directions to advance research towards the aim of effective and broadly applicable STFMs.

Figures

Figures reproduced from arXiv: 2501.09045 by the authors.

Figure 1
Figure 1. The four types of spatio-temporal data (raster, point reference, trajectory and events), with example use cases and illustrations. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. A framework for spatio-temporal foundation models (STFMs). Top-left: STFMs can flexibly handle various forms of ST data as input (see Section [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Four forms of generalization in spatio-temporal data. Top-left: domain generalization across different sources of data representing different physical [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: A selection of spatio-temporal domains that STFMs should be able to generalize across. Dashed lines indicate examples of potential for correlations [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning Spatio-Temporal Foundation Models from Pure Synthetic Data

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    A spatio-temporal foundation model pre-trained exclusively on synthetic stochastic graph dynamics outperforms real-data-pretrained STFMs in zero-shot traffic forecasting, according to the paper's benchmarks.

  2. SciVid: Cross-Domain Evaluation of Video Models in Scientific Applications

    cs.CV 2025-07 conditional novelty 6.0 of 10

    General-purpose video foundation models, adapted with lightweight readout heads, reach state-of-the-art performance on three of five scientific video benchmarks.

  3. Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications

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    The paper defines urban LLM agents, surveys their sensing, memory, reasoning, execution, and learning workflows, and organizes their applications across planning, transportation, environment, safety, and society.

  4. Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review

    cs.LG 2025-06 conditional novelty 4.0 of 10

    Spatio-temporal foundation models are organized into a pipeline of data harmonization, model design, training, and adaptation, with a data property taxonomy for model selection.

  5. Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive Hypergraphs

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    STH-SepNet decouples temporal and spatial modeling for spatio-temporal prediction using a small LLM for time and a KNN-built adaptive hypergraph for space, and reports state-of-the-art MAE/RMSE on five traffic datasets.

Reference graph

Works this paper leans on

83 extracted references · 54 canonical work pages · cited by 5 Pith papers

  1. [1]

    Regional air quality forecasting using spa- tiotemporal deep learning

    S Abirami and P Chitra. Regional air quality forecasting using spa- tiotemporal deep learning. Journal of Cleaner Production , 283:125341, 2021

    2021

  2. [2]

    A spatio-temporal transformer for 3d human motion prediction

    Emre Aksan, Manuel Kaufmann, Peng Cao, and Otmar Hilliges. A spatio-temporal transformer for 3d human motion prediction. In 2021 International Conference on 3D Vision (3DV) , pages 565–574. IEEE, 2021

    2021

  3. [3]

    Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction

    Ahmad Ali, Yanmin Zhu, and Muhammad Zakarya. Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction. Neural networks, 145:233–247, 2022

    2022

  4. [4]

    A novel framework for spatio-temporal prediction of en- vironmental data using deep learning

    Federico Amato, Fabian Guignard, Sylvain Robert, and Mikhail Kanevski. A novel framework for spatio-temporal prediction of en- vironmental data using deep learning. Scientific reports , 10(1):22243, 2020

    work page 2020

  5. [5]

    Bi-directional convlstm u-net with densley connected convo- lutions

    Reza Azad, Maryam Asadi-Aghbolaghi, Mahmood Fathy, and Sergio Escalera. Bi-directional convlstm u-net with densley connected convo- lutions. In Proceedings of the IEEE/CVF international conference on computer vision workshops , pages 0–0, 2019

    work page 2019

  6. [6]

    Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast

    Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, and Qi Tian. Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast. arXiv preprint arXiv:2211.02556 , 2022

    arXiv 2022

  7. [7]

    A graph cnn-lstm neural network for short and long-term traffic forecasting based on trajectory data

    Toon Bogaerts, Antonio D Masegosa, Juan S Angarita-Zapata, Enrique Onieva, and Peter Hellinckx. A graph cnn-lstm neural network for short and long-term traffic forecasting based on trajectory data. Transportation Research Part C: Emerging Technologies, 112:62–77, 2020

    work page 2020

  8. [8]

    On the opportunities and risks of foundation models

    Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 , 2021

    arXiv 2021

Show all 83 references
  1. [9]

    Language models are few-shot learners

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems , 33:1877–1901, 2020

    1901

  2. [10]

    Stconvs2s: Spatiotemporal convolutional sequence to sequence network for weather forecasting

    Rafaela Castro, Yania M Souto, Eduardo Ogasawara, Fabio Porto, and Eduardo Bezerra. Stconvs2s: Spatiotemporal convolutional sequence to sequence network for weather forecasting. Neurocomputing, 426:285– 298, 2021

    2021

  3. [11]

    Adapting pretrained vision-language foundational models to medical imaging domains

    Pierre Chambon, Christian Bluethgen, Curtis P Langlotz, and Akshay Chaudhari. Adapting pretrained vision-language foundational models to medical imaging domains. arXiv preprint arXiv:2210.04133 , 2022

    2022 arXiv

  4. [12]

    Improving yield prediction based on spatio- temporal deep learning approaches for winter wheat: A case study in jiangsu province, china

    Peipei Chen, Yue Li, Xiaojun Liu, Yongchao Tian, Yan Zhu, Weixing Cao, and Qiang Cao. Improving yield prediction based on spatio- temporal deep learning approaches for winter wheat: A case study in jiangsu province, china. Computers and Electronics in Agriculture , 213:108201, 2023

    2023

  5. [13]

    A hybrid cnn-lstm model for typhoon formation forecasting

    Rui Chen, Xiang Wang, Weimin Zhang, Xiaoyu Zhu, Aiping Li, and Chao Yang. A hybrid cnn-lstm model for typhoon formation forecasting. GeoInformatica, 23:375–396, 2019

    2019

  6. [14]

    Terra: A multimodal spatio- temporal dataset spanning the earth

    Wei Chen, Xixuan Hao, Yuxuan Liang, et al. Terra: A multimodal spatio- temporal dataset spanning the earth. In The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track

  7. [15]

    Toward causal inference for spatio-temporal data: Conflict and forest loss in colombia

    Rune Christiansen, Matthias Baumann, Tobias Kuemmerle, Miguel D Mahecha, and Jonas Peters. Toward causal inference for spatio-temporal data: Conflict and forest loss in colombia. Journal of the American Statistical Association, 117(538):591–601, 2022

    2022

  8. [16]

    Causal structure discovery for spatio-temporal data

    Victor W Chu, Raymond K Wong, Wei Liu, and Fang Chen. Causal structure discovery for spatio-temporal data. In International Confer- ence on Database Systems for Advanced Applications , pages 236–250. Springer, 2014

    2014

  9. [17]

    On the use of remote sensing techniques for monitoring spatio-temporal soil organic carbon dynamics in agricultural systems

    H Croft, NJ Kuhn, and K Anderson. On the use of remote sensing techniques for monitoring spatio-temporal soil organic carbon dynamics in agricultural systems. Catena, 94:64–74, 2012

    2012

  10. [18]

    Bert: Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 , 2018

    2018 arXiv

  11. [19]

    Understanding the role of weather data for earth surface forecasting using a convlstm-based model

    Codrut ,-Andrei Diaconu, Sudipan Saha, Stephan G ¨unnemann, and Xiao Xiang Zhu. Understanding the role of weather data for earth surface forecasting using a convlstm-based model. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 1362–...

    2022

  12. [20]

    Spatio-temporal data mining for climate data: Advances, challenges, and opportunities

    James H Faghmous and Vipin Kumar. Spatio-temporal data mining for climate data: Advances, challenges, and opportunities. Data mining and knowledge discovery for big data: Methodologies, challenge and opportunities, pages 83–116, 2014

    2014

  13. [21]

    Long-range transformers for dynamic spatiotemporal forecasting

    Jake Grigsby, Zhe Wang, Nam Nguyen, and Yanjun Qi. Long-range transformers for dynamic spatiotemporal forecasting. arXiv preprint arXiv:2109.12218, 2021

    2021 arXiv

  14. [22]

    Deepseek-r1: Incentivizing reasoning capability in llms via reinforce- ment learning

    Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforce- ment learning. arXiv preprint arXiv:2501.12948 , 2025

    2025 arXiv

  15. [23]

    Sub-seasonal climate forecasting via machine learning: Chal- lenges, analysis, and advances

    Sijie He, Xinyan Li, Timothy DelSole, Pradeep Ravikumar, and Arindam Banerjee. Sub-seasonal climate forecasting via machine learning: Chal- lenges, analysis, and advances. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 35, pages 169–177, 2021

    2021

  16. [24]

    The era5 global reanalysis

    Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, Andr ´as Hor´anyi, Joaqu´ın Mu˜noz-Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, et al. The era5 global reanalysis. Quarterly Journal of the Royal Meteorological Society , 146(730):1999–2049, 2020

    1999

  17. [25]

    Scaling up visual and vision-language representation learning with noisy text supervision

    Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International conference on machine learning , pages 4904–4916...

    2021

  18. [26]

    Spatio-temporal graph neural networks for predictive learning in urban computing: A survey

    Guangyin Jin, Yuxuan Liang, Yuchen Fang, Zezhi Shao, Jincai Huang, Junbo Zhang, and Yu Zheng. Spatio-temporal graph neural networks for predictive learning in urban computing: A survey. IEEE Transactions on Knowledge and Data Engineering , 2023

    2023

  19. [27]

    Time-llm: Time series forecasting by reprogramming large language models

    Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, et al. Time-llm: Time series forecasting by reprogramming large language models. arXiv preprint arXiv:2310.01728 , 2023

    arXiv

  20. [28]

    Examining covid-19 forecast- ing using spatio-temporal graph neural networks

    Amol Kapoor, Xue Ben, Luyang Liu, Bryan Perozzi, Matt Barnes, Martin Blais, and Shawn O’Banion. Examining covid-19 forecast- ing using spatio-temporal graph neural networks. arXiv preprint arXiv:2007.03113, 2020

    2007 arXiv

  21. [29]

    Self- regulating prompts: Foundational model adaptation without forgetting

    Muhammad Uzair Khattak, Syed Talal Wasim, Muzammal Naseer, Salman Khan, Ming-Hsuan Yang, and Fahad Shahbaz Khan. Self- regulating prompts: Foundational model adaptation without forgetting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15190–...

    2023

  22. [30]

    Deeprain: Convlstm network for precipitation prediction using multi- channel radar data

    Seongchan Kim, Seungkyun Hong, Minsu Joh, and Sa-kwang Song. Deeprain: Convlstm network for precipitation prediction using multi- channel radar data. arXiv preprint arXiv:1711.02316 , 2017

    2017 arXiv

  23. [31]

    Segment anything

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 4015– 4026, 2023

    2023

  24. [32]

    Uniformer: Unified transformer for efficient spatiotem- poral representation learning

    Kunchang Li, Yali Wang, Peng Gao, Guanglu Song, Yu Liu, Hongsheng Li, and Yu Qiao. Uniformer: Unified transformer for efficient spatiotem- poral representation learning. arXiv preprint arXiv:2201.04676 , 2022

    2022 arXiv

  25. [33]

    Urbangpt: Spatio-temporal large language models

    Zhonghang Li, Lianghao Xia, Jiabin Tang, Yong Xu, Lei Shi, Long Xia, Dawei Yin, and Chao Huang. Urbangpt: Spatio-temporal large language models. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages 5351–5362, 2024

    2024

  26. [34]

    Opencity: Open spatio-temporal foundation models for traffic prediction

    Zhonghang Li, Long Xia, Lei Shi, Yong Xu, Dawei Yin, and Chao Huang. Opencity: Open spatio-temporal foundation models for traffic prediction. arXiv preprint arXiv:2408.10269 , 2024

    2024

  27. [35]

    Foundation models for time series analysis: A tutorial and survey

    Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, and Qingsong Wen. Foundation models for time series analysis: A tutorial and survey. In Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining , pages 6555–6565, 2024

    2024

  28. [36]

    Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning

    Yijun Lin, Nikhit Mago, Yu Gao, Yaguang Li, Yao-Yi Chiang, Cyrus Shahabi, and Jos ´e Luis Ambite. Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning. In Proceedings of the 26th ACM SIGSPATIAL international conference on advances in geog...

    2018

  29. [37]

    Self-attention convlstm for spatiotemporal prediction

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

    2020

  30. [38]

    Few-shot adaptation of multi-modal foundation models: A survey

    Fan Liu, Tianshu Zhang, Wenwen Dai, Chuanyi Zhang, Wenwen Cai, Xiaocong Zhou, and Delong Chen. Few-shot adaptation of multi-modal foundation models: A survey. Artificial Intelligence Review, 57(10):268, 2024

    2024

  31. [39]

    Dis- covering spatio-temporal causal interactions in traffic data streams

    Wei Liu, Yu Zheng, Sanjay Chawla, Jing Yuan, and Xie Xing. Dis- covering spatio-temporal causal interactions in traffic data streams. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining , pages 1010–1018, 2011

    2011

  32. [40]

    Lsttn: A long-short term transformer-based spatiotemporal neural network for traffic flow forecasting

    Qinyao Luo, Silu He, Xing Han, Yuhan Wang, and Haifeng Li. Lsttn: A long-short term transformer-based spatiotemporal neural network for traffic flow forecasting. Knowledge-Based Systems, 293:111637, 2024

    2024

  33. [41]

    Histgnn: Hierarchical spatio-temporal graph neural network for weather forecasting

    Minbo Ma, Peng Xie, Fei Teng, Bin Wang, Shenggong Ji, Junbo Zhang, and Tianrui Li. Histgnn: Hierarchical spatio-temporal graph neural network for weather forecasting. Information Sciences , 648:119580, 2023

    2023

  34. [42]

    W- mae: Pre-trained weather model with masked autoencoder for multi- variable weather forecasting

    Xin Man, Chenghong Zhang, Jin Feng, Changyu Li, and Jie Shao. W- mae: Pre-trained weather model with masked autoencoder for multi- variable weather forecasting. arXiv preprint arXiv:2304.08754 , 2023

    2023 arXiv

  35. [43]

    Artificial intelligence for climate prediction of extremes: State of the art, challenges, and fu- ture perspectives

    Stefano Materia, Llu ´ıs Palma Garc ´ıa, Chiem van Straaten, Sungmin O, Antonios Mamalakis, Leone Cavicchia, Dim Coumou, Paolo de Luca, Marlene Kretschmer, and Markus Donat. Artificial intelligence for climate prediction of extremes: State of the art, challenges, and fu- ture ...

    2024

  36. [44]

    The coupled model intercomparison project (cmip)

    Gerald A Meehl, George J Boer, Curt Covey, Mojib Latif, and Ronald J Stouffer. The coupled model intercomparison project (cmip). Bulletin of the American Meteorological Society , 81(2):313–318, 2000

    2000

  37. [45]

    Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction

    Abduallah Mohamed, Kun Qian, Mohamed Elhoseiny, and Christian Claudel. Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14424–...

    2020

  38. [46]

    Crop yield prediction using multitemporal uav data and spatio-temporal deep learning models

    Petteri Nevavuori, Nathaniel Narra, Petri Linna, and Tarmo Lipping. Crop yield prediction using multitemporal uav data and spatio-temporal deep learning models. Remote Sensing, 12(23):4000, 2020

    2020

  39. [47]

    Climax: A foundation model for weather and climate

    Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K Gupta, and Aditya Grover. Climax: A foundation model for weather and climate. arXiv preprint arXiv:2301.10343 , 2023

    2023 arXiv

  40. [48]

    Spatio-temporal prediction of the covid-19 pandemic in us counties: modeling with a deep lstm neural network

    Behnam Nikparvar, Md Mokhlesur Rahman, Faizeh Hatami, and Jean- Claude Thill. Spatio-temporal prediction of the covid-19 pandemic in us counties: modeling with a deep lstm neural network. Scientific reports, 11(1):21715, 2021

    2021

  41. [49]

    Urban traffic prediction from spatio-temporal data using deep meta learning

    Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, and Junbo Zhang. Urban traffic prediction from spatio-temporal data using deep meta learning. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining , pages 1720–1730, 2019

    2019

  42. [50]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning , p...

    2021

  43. [51]

    Lag-llama: Towards foun- dation models for time series forecasting

    Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Bilo ˇs, Hena Ghonia, Nadhir Hassen, Anderson Schneider, et al. Lag-llama: Towards foun- dation models for time series forecasting. In R0-FoMo: Robustness of Few-s...

    2023

  44. [52]

    Deep learning-based weather prediction: a survey

    Xiaoli Ren, Xiaoyong Li, Kaijun Ren, Junqiang Song, Zichen Xu, Kefeng Deng, and Xiang Wang. Deep learning-based weather prediction: a survey. Big Data Research , 23:100178, 2021

    2021

  45. [53]

    Spatio-temporal graph neural networks: A survey

    Zahraa Al Sahili and Mariette Awad. Spatio-temporal graph neural networks: A survey. arXiv preprint arXiv:2301.10569 , 2023

    2023 arXiv

  46. [54]

    Decoupled dynamic spatial-temporal graph neural network for traffic forecasting

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

    2022 arXiv

  47. [55]

    Convolutional lstm network: A machine learning approach for precipitation nowcasting

    Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. Convolutional lstm network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems , 28, 2015

    2015

  48. [56]

    Spatio-temporal analysis of influenza-like illness and prediction of incidence in high-risk regions in the united states from 2011 to 2020

    Zhijuan Song, Xiaocan Jia, Junzhe Bao, Yongli Yang, Huili Zhu, and Xuezhong Shi. Spatio-temporal analysis of influenza-like illness and prediction of incidence in high-risk regions in the united states from 2011 to 2020. International Journal of Environmental Research and Publ...

    2011

  49. [57]

    Era5 hourly data on single levels from 1979 to present

    Copernicus Climate Data Store. Era5 hourly data on single levels from 1979 to present. URL: https://cds. climate. copernicus. eu/cdsapp, 2021

    1979

  50. [58]

    Adaptive spatio-temporal graph neural network for traffic forecasting

    Xuxiang Ta, Zihan Liu, Xiao Hu, Le Yu, Leilei Sun, and Bowen Du. Adaptive spatio-temporal graph neural network for traffic forecasting. Knowledge-based systems, 242:108199, 2022

    2022

  51. [59]

    A survey on mod- ern deep neural network for traffic prediction: Trends, methods and challenges

    David Alexander Tedjopurnomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, and Alex Kai Qin. A survey on mod- ern deep neural network for traffic prediction: Trends, methods and challenges. IEEE Transactions on Knowledge and Data Engineering , 34(4):1544–1561, 2020

    2020

  52. [60]

    Llama: Open and efficient foundation language models

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie- Anne Lachaux, Timoth´ee Lacroix, Baptiste Rozi `ere, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 , 2023

    2023 arXiv

  53. [61]

    Attention is all you need

    A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017

    2017

  54. [62]

    Deepstcl: A deep spatio-temporal convlstm for travel demand prediction

    Dongjie Wang, Yan Yang, and Shangming Ning. Deepstcl: A deep spatio-temporal convlstm for travel demand prediction. In 2018 inter- national joint conference on neural networks (IJCNN), pages 1–8. IEEE, 2018

    2018

  55. [63]

    Causalgnn: Causal-based graph neural networks for spatio-temporal epidemic forecasting

    Lijing Wang, Aniruddha Adiga, Jiangzhuo Chen, Adam Sadilek, Srini- vasan Venkatramanan, and Madhav Marathe. Causalgnn: Causal-based graph neural networks for spatio-temporal epidemic forecasting. In Proceedings of the AAAI conference on artificial intelligence, volume 36, page...

    2022

  56. [64]

    Predicting the spatio-temporal evolution of chronic diseases in population with human mobility data

    Yingzi Wang, Xiao Zhou, Cecilia Mascolo, Anastasios Noulas, Xing Xie, and Qi Liu. Predicting the spatio-temporal evolution of chronic diseases in population with human mobility data. IJCAI, 2018

    2018

  57. [65]

    Deep learning of spatiotem- poral patterns for urban mobility prediction using big data

    Yun Wang, Faiz Currim, and Sudha Ram. Deep learning of spatiotem- poral patterns for urban mobility prediction using big data. Information Systems Research, 33(2):579–598, 2022

    2022

  58. [66]

    A novel spatiotemporal convolutional long short- term neural network for air pollution prediction

    Congcong Wen, Shufu Liu, Xiaojing Yao, Ling Peng, Xiang Li, Yuan Hu, and Tianhe Chi. A novel spatiotemporal convolutional long short- term neural network for air pollution prediction. Science of the total environment, 654:1091–1099, 2019

    2019

  59. [67]

    Unified training of universal time series forecasting transformers

    Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, and Doyen Sahoo. Unified training of universal time series forecasting transformers. arXiv preprint arXiv:2402.02592 , 2024

    2024 arXiv

  60. [68]

    Deciphering spatio-temporal graph forecasting: A causal lens and treatment

    Yutong Xia, Yuxuan Liang, Haomin Wen, Xu Liu, Kun Wang, Zhengyang Zhou, and Roger Zimmermann. Deciphering spatio-temporal graph forecasting: A causal lens and treatment. Advances in Neural Information Processing Systems , 36, 2024

    2024

  61. [69]

    Urban flow prediction from spatiotemporal data using machine learning: A survey

    Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, and Junbo Zhang. Urban flow prediction from spatiotemporal data using machine learning: A survey. Information Fusion, 59:1–12, 2020

    2020

  62. [70]

    Spatiotemporal forecasting in earth system science: Methods, un- certainties, predictability and future directions

    Lei Xu, Nengcheng Chen, Zeqiang Chen, Chong Zhang, and Hongchu Yu. Spatiotemporal forecasting in earth system science: Methods, un- certainties, predictability and future directions. Earth-Science Reviews, 222:103828, 2021

    2021

  63. [71]

    Spatio-temporal prediction of crop disease severity for agricultural emergency management based on recurrent neural networks

    Wei Xu, Qili Wang, and Runyu Chen. Spatio-temporal prediction of crop disease severity for agricultural emergency management based on recurrent neural networks. GeoInformatica, 22:363–381, 2018

    2018

  64. [72]

    Learning spatio-temporal transformer for visual tracking

    Bin Yan, Houwen Peng, Jianlong Fu, Dong Wang, and Huchuan Lu. Learning spatio-temporal transformer for visual tracking. In Proceedings of the IEEE/CVF international conference on computer vision , pages 10448–10457, 2021

    2021

  65. [73]

    Multi-hour and multi-site air quality index forecasting in beijing using cnn, lstm, cnn-lstm, and spatiotemporal clustering

    Rui Yan, Jiaqiang Liao, Jie Yang, Wei Sun, Mingyue Nong, and Feipeng Li. Multi-hour and multi-site air quality index forecasting in beijing using cnn, lstm, cnn-lstm, and spatiotemporal clustering. Expert Systems with Applications, 169:114513, 2021

    2021

  66. [74]

    Spatio-temporal graph con- volutional networks: A deep learning framework for traffic forecasting

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

    2017 arXiv

  67. [75]

    Spatio- temporal graph transformer networks for pedestrian trajectory prediction

    Cunjun Yu, Xiao Ma, Jiawei Ren, Haiyu Zhao, and Shuai Yi. Spatio- temporal graph transformer networks for pedestrian trajectory prediction. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XII 16 , pages 507–523. Springer, 2020

    2020

  68. [76]

    Spatio-temporal graph learning for epidemic prediction

    Shuo Yu, Feng Xia, Shihao Li, Mingliang Hou, and Quan Z Sheng. Spatio-temporal graph learning for epidemic prediction. ACM Transac- tions on Intelligent Systems and Technology , 14(2):1–25, 2023

    2023

  69. [77]

    Unist: A prompt-empowered universal model for urban spatio-temporal pre- diction

    Yuan Yuan, Jingtao Ding, Jie Feng, Depeng Jin, and Yong Li. Unist: A prompt-empowered universal model for urban spatio-temporal pre- diction. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages 4095–4106, 2024

    2024

  70. [78]

    A foundation model for unified urban spatio-temporal flow prediction

    Yuan Yuan, Jingtao Ding, Chonghua Han, Depeng Jin, and Yong Li. A foundation model for unified urban spatio-temporal flow prediction. arXiv preprint arXiv:2411.12972 , 2024

    2024 arXiv

  71. [79]

    Urbandit: A foundation model for open-world urban spatio-temporal learning

    Yuan Yuan, Chonghua Han, Jingtao Ding, Depeng Jin, and Yong Li. Urbandit: A foundation model for open-world urban spatio-temporal learning. arXiv preprint arXiv:2411.12164 , 2024

    2024

  72. [80]

    Artificial intelligence– enabled public health surveillance—from local detection to global epi- demic monitoring and control

    Daniel Zeng, Zhidong Cao, and Daniel B Neill. Artificial intelligence– enabled public health surveillance—from local detection to global epi- demic monitoring and control. In Artificial intelligence in medicine , pages 437–453. Elsevier, 2021

    2021

  73. [81]

    Spatio-temporal graph structure learning for traffic forecast- ing

    Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, and Chun- hong Pan. Spatio-temporal graph structure learning for traffic forecast- ing. In Proceedings of the AAAI conference on artificial intelligence , volume 34, pages 1177–1185, 2020

    2020

  74. [82]

    Generative causal interpretation model for spatio-temporal representa- tion learning

    Yu Zhao, Pan Deng, Junting Liu, Xiaofeng Jia, and Jianwei Zhang. Generative causal interpretation model for spatio-temporal representa- tion learning. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages 3537–3548, 2023

    2023

  75. [83]

    pg-causality: Identifying spatiotemporal causal pathways for air pollutants with urban big data

    Julie Yixuan Zhu, Chao Zhang, Huichu Zhang, Shi Zhi, Victor OK Li, Jiawei Han, and Yu Zheng. pg-causality: Identifying spatiotemporal causal pathways for air pollutants with urban big data. IEEE Transac- tions on Big Data , 4(4):571–585, 2017

    2017

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.