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REVIEW 3 major objections 3 minor 56 references

Multi-grained spatial-temporal feature complementarity for accurate online cellular traffic prediction

T0 review · 3 major / 3 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that MGSTC, an online cellular traffic predictor combining coarse-grained temporal attention, fine-grained spatial attention, and real-time concept-drift detection, consistently outperforms eleven state-of-the-art…

desk verdict The submission's full text is a different paper, so the actual MGSTC claims are unverifiable—treat this as a defective upload, not a citable result. read the letter →

arxiv 2508.08281 v1 pith:XE6RMR6F submitted 2025-08-01 cs.LG cs.AIcs.NI

Ningning Fu , Shengheng Liu , Weiliang Xie , Yongming Huang This is my paper · ORCID
classification cs.LGcs.AIcs.NI
keywords cellulartrafficpredictiononlinelearningconceptdriftspatiotemporalattentioncoarse-grainedtemporalfine-grainedspatialmulti-grainedfeaturecomplementaritytelecomnetworkmanagement
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

The paper aims to show that cellular traffic forecasting in continuous, streaming settings can be made substantially more accurate by combining two complementary views: a coarse-grained temporal view that reads the broad trend from chunks of history, and a fine-grained spatial view that adjusts the trend using local correlations among network elements. It also argues that an online learning strategy with real-time concept-drift detection and stage-switching parameter updates is needed to keep forecasts accurate as the data distribution shifts. If the claims are right, telecom operators could schedule resources and manage traffic proactively without constant manual intervention, and the method would be a strong benchmark for online spatiotemporal forecasting.

What carries the argument

The central object is the MGSTC architecture, built on multi-grained spatial-temporal feature complementarity. Historical data is first segmented into chunks; a coarse-grained temporal attention module reads these chunks to supply a trend reference for the prediction horizon. A fine-grained spatial attention module then captures detailed correlations among network elements and refines the trend locally. A real-time concept-drift detector monitors the streaming input and triggers a switch to the appropriate parameter-update stage, which is the mechanism that keeps the model accurate during continuous forecasting.

What would settle it

Re-run the method under a strictly causal protocol where every state at time t depends only on observations available at time t, re-tune all eleven baselines under the same protocol, and compare; if MGSTC no longer consistently outperforms the baselines on the four datasets, the central claim is refuted.

Watch

Extended reading notes

Core claim

The central discovery claimed is that coarse-grained temporal attention and fine-grained spatial attention are complementary: the first provides a stable trend reference over the prediction horizon, the second provides localized refinement using detailed spatial correlations, and their complementarity permits efficient transmission of valuable information. On top of this, the paper introduces an online learning strategy that detects concept drift in real time and switches to the appropriate parameter-update stage, allowing the model to maintain high precision in continuous forecasting. The paper reports that this method, MGSTC, consistently outperforms eleven state-of-the-art baselines on four real-world datasets.

Load-bearing premise

The method must never use information from the prediction horizon when detecting drift or deciding when to update parameters; if any future information leaks into those decisions, the reported accuracy could be an artifact rather than a genuine online prediction gain.

Editorial extensions

If this is right

  • If MGSTC is correct, telecom operators can continuously anticipate traffic demand and adjust scheduling and resource allocation without frequent manual retuning.
  • The coarse-to-fine design suggests that explicit trend references can stabilize longer-horizon predictions in streaming settings.
  • Real-time concept-drift detection and dynamic parameter-update switching would be a reusable recipe for other non-stationary forecasting tasks.
  • The reported consistent outperformance across four real-world datasets supports treating MGSTC as a reference point for future online cellular traffic predictors.

Reading between the lines

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

  • The paper's abstract does not report statistical significance or error bars; a natural next step would be to test whether the gains over the eleven baselines are significant under repeated runs.
  • The combination of chunked temporal attention with fine spatial refinement may transfer to other bursty spatiotemporal signals such as energy demand or urban mobility, though the paper does not investigate that.
  • The concept-drift detector's threshold and the switch rule are the most likely places where causal leakage could enter; an independent check should verify that they are tuned only on historical data, not on the prediction horizon.
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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 / 3 minor

Summary. The submitted manuscript, titled 'Multi-grained spatial-temporal feature complementarity for accurate online cellular traffic prediction', presents an abstract proposing MGSTC, an online cellular traffic prediction method that combines coarse-grained temporal attention, fine-grained spatial attention, and real-time concept-drift detection to switch parameter-update stages. The abstract claims that experiments on four real-world datasets show consistent outperformance over eleven state-of-the-art baselines. However, the full text attached to the submission is a completely different manuscript, a game-theory paper titled 'Coordinating cooperation in stag-hunt game' (arXiv:2508.08301). The submission therefore does not contain the methods, experimental protocol, datasets, baseline configurations, or results of the MGSTC work, leaving all substantive claims unverifiable from the provided material.

Significance. The claimed contribution is empirical and comparative, and if the missing MGSTC manuscript were present and its claims held, it would be a useful addition to online cellular traffic forecasting. The proposed combination of multi-grained attention and concept-drift-driven online updates is a plausible direction, and the abstract's framing of concept drift as a challenge in continuous forecasting is reasonable. However, the significance cannot be assessed from this submission because the technical content is entirely absent: no model architecture is defined, no dataset names or characteristics are given, no baseline tuning or protocol is described, and no quantitative results or code are provided. The paper's value, if any, rests entirely on an unverifiable comparative claim.

major comments (3)
  1. [Full text] The full text of the submission is arXiv:2508.08301, 'Coordinating cooperation in stag-hunt game', a physics/game-theory manuscript with no connection to cellular traffic prediction. The central claim that MGSTC outperforms eleven state-of-the-art baselines on four real-world datasets cannot be checked because the submission contains no model specification, no dataset descriptions, no experimental protocol, no ablation studies, and no results tables. This is a missing-support condition that blocks all evaluation of the manuscript's central claim.
  2. [Abstract] The abstract makes a strong comparative claim but reports no quantitative outcomes. It does not name the four datasets, the eleven baselines, the evaluation metrics, or any error bars or statistical significance tests. Without at least summary performance numbers, the abstract alone cannot support the assertion of consistent superiority, and the absence of results in the provided full text makes the claim entirely unverifiable.
  3. [Abstract] The online prediction claim rests on a causality premise: that the chunking of historical data, the concept-drift detector, and the switch to the 'appropriate parameter update stage' never use information from the prediction horizon. Because the provided full text does not describe the algorithm, this premise cannot be inspected, and the correctness of the online protocol remains an unverified load-bearing assumption. A concrete test would require specifying how drift thresholds and update-stage selection are set relative to the test period, but no such details are present.
minor comments (3)
  1. [Abstract] The abstract would be more informative if it listed the names of the four real-world datasets and the eleven baselines, along with the evaluation metric used; these details are standard for comparative papers and would help readers assess the claim even before reading the full text.
  2. [Title vs. Full text] The title of the submitted paper does not match the title of the attached full text, indicating that the wrong file was likely included in the submission package; the authors should verify that the correct manuscript is attached.
  3. [Abstract] The terms 'coarse-grained temporal attention' and 'fine-grained spatial attention' are introduced without definition or references; the reader cannot determine what these components are or how they interact, and no references are provided to place them in the literature.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found; the abstract's empirical benchmark claim is not derivationally circular, though the supplied full text is a different paper and prevents verification.

full rationale

The manuscript under review presents an abstract for arXiv:2508.08281 (MGSTC) but the supplied full text is arXiv:2508.08301, a stag-hunt game theory paper, so the MGSTC methods, equations, and experimental protocol are not available for inspection. This is a completeness or missing-support condition, not a circularity condition. The abstract's central claim is comparative and empirical: 'MGSTC outperforms eleven state-of-the-art baselines consistently' on four real-world datasets. Such a claim is externally falsifiable and would stand or fall on dataset selection, baseline tuning, and protocol fairness; it does not reduce by construction to a fitted input or to a self-citation. No equations are present, no parameter is called a prediction after being fit to the target quantity, and no uniqueness theorem or ansatz is imported from the authors' prior work. The online-learning concerns raised by the reader, such as possible lookahead in drift detection or update-stage selection, are correctness risks that cannot be evaluated from the supplied text; they are not evidence of circularity under the hard rules, which require quoting a specific reduction. Because no derivation chain is present to inspect and no circular step can be exhibited, the honest finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

Abstract-only audit. The supplied full text is an unrelated manuscript, so hyperparameter values, dataset details, and the training procedure could not be audited. The free parameters listed are inferred from the abstract's own descriptions of chunking, drift detection, and attention.

free parameters (4)
  • Historical chunk size
    MGSTC 'segments historical data into chunks' (abstract); the chunk length controls the trend reference and is a hand-chosen hyperparameter not quantified in the abstract.
  • Concept drift detection threshold
    The online strategy 'detect[s] concept drift in real-time and promptly switches to the appropriate parameter update stage' (abstract); the trigger threshold is a free parameter that determines how often parameters are updated.
  • Update stage selection criteria
    The abstract implies discrete parameter-update stages; the criteria for choosing the 'appropriate' stage are not specified and constitute a modeling choice.
  • Attention architecture hyperparameters
    Dimensions, heads, and fusion weights for the coarse- and fine-grained attention modules are not given in the abstract; these are standard free parameters for such networks.
assumptions (3)
  • domain assumption The four real-world datasets are representative of online cellular traffic and the evaluation protocol is causal.
    The abstract claims results on four real-world datasets but gives no splits, leakage controls, or protocol details; the online claim depends on no lookahead.
  • domain assumption The eleven baselines are implemented and tuned fairly under the identical online setting.
    'Outperforms eleven state-of-the-art baselines consistently' (abstract) presumes fair comparison; no tuning or protocol details are given.
  • ad hoc to paper Chunked history retains a stable trend reference under concept drift.
    The method's coarse-grained temporal attention relies on historical chunks to provide a 'trend reference' (abstract); this presumes the trend structure survives drift long enough to be useful.

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Cite this review

Pith. "Pith review of Multi-grained spatial-temporal feature complementarity for accurate online cellular traffic prediction." pith.science (2026). https://pith.science/paper/XE6RMR6F

@misc{pith2026250808281,
  author       = {Pith},
  title        = {Pith review of: Multi-grained spatial-temporal feature complementarity for accurate online cellular traffic prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XE6RMR6F}},
  note         = {Machine review of arXiv:2508.08281}
}
read the original abstract

Knowledge discovered from telecom data can facilitate proactive understanding of network dynamics and user behaviors, which in turn empowers service providers to optimize cellular traffic scheduling and resource allocation. Nevertheless, the telecom industry still heavily relies on manual expert intervention. Existing studies have been focused on exhaustively explore the spatial-temporal correlations. However, they often overlook the underlying characteristics of cellular traffic, which are shaped by the sporadic and bursty nature of telecom services. Additionally, concept drift creates substantial obstacles to maintaining satisfactory accuracy in continuous cellular forecasting tasks. To resolve these problems, we put forward an online cellular traffic prediction method grounded in Multi-Grained Spatial-Temporal feature Complementarity (MGSTC). The proposed method is devised to achieve high-precision predictions in practical continuous forecasting scenarios. Concretely, MGSTC segments historical data into chunks and employs the coarse-grained temporal attention to offer a trend reference for the prediction horizon. Subsequently, fine-grained spatial attention is utilized to capture detailed correlations among network elements, which enables localized refinement of the established trend. The complementarity of these multi-grained spatial-temporal features facilitates the efficient transmission of valuable information. To accommodate continuous forecasting needs, we implement an online learning strategy that can detect concept drift in real-time and promptly switch to the appropriate parameter update stage. Experiments carried out on four real-world datasets demonstrate that MGSTC outperforms eleven state-of-the-art baselines consistently.

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Reference graph

Works this paper leans on

56 extracted references · 54 canonical work pages

  1. [1]

    Toward 6G TK extreme connectivity: Architecture, key technologies and experiments

    Xiaohu You, Yongming Huang, Shengheng Liu, Dongming Wang, Junchao Ma, Chuan Zhang, Hang Zhan, Cheng Zhang, Jiao Zhang, Zening Liu, Jin Li, Min Zhu, Jianjie You, Dongjie Liu, Yang Cao, Shiwen He, Guanghui He, Fengyi Yang, Yang Liu, Jianjun Wu, Jianmin Lu, Ge Li, Xiaowu Chen, Wenguang Chen, and Wen Gao. Toward 6G TK extreme connectivity: Architecture, key t...

    work page 2023

  2. [2]

    Deep learning on network traffic prediction: Recent advances, analysis, and future directions

    Ons Aouedi, Van An Le, Kandaraj Piamrat, and Yusheng Ji. Deep learning on network traffic prediction: Recent advances, analysis, and future directions. ACM Comput. Surv. , Jan. 2025. Just Accepted

    work page 2025

  3. [3]

    Cellular traffic prediction with machine learning: A survey

    Weiwei Jiang. Cellular traffic prediction with machine learning: A survey. Expert Syst. Appl. , 201:117163, Mar. 2022

    work page 2022

  4. [4]

    Vu, and Symeon Chatzinotas

    Fatemeh Kavehmadavani, Van-Dinh Nguyen, Thang X. Vu, and Symeon Chatzinotas. Intelligent traffic steering in beyond 5G open RAN based on LSTM traffic prediction. IEEE Trans. Wireless Commun. , 22(11):7727--7742, Nov. 2023

    work page 2023

  5. [5]

    Multi-range bidirectional mask graph convolution based gru networks for traffic prediction

    Na Hu, Dafang Zhang, Kun Xie, Wei Liang, Chunyan Diao, and Kuan-Ching Li. Multi-range bidirectional mask graph convolution based gru networks for traffic prediction. J. Syst. Archit. , 133:102775, Dec. 2022

    work page 2022

  6. [6]

    A novel STFSA-CNN-GRU hybrid model for short-term traffic speed prediction

    Changxi Ma, Yongpeng Zhao, Guowen Dai, Xuecai Xu, and Sze-Chun Wong. A novel STFSA-CNN-GRU hybrid model for short-term traffic speed prediction. IEEE Trans. Intell. Transp. Syst. , 24(4):3728--3737, Apr. 2023

    work page 2023

  7. [7]

    A hybrid prediction method for realistic network traffic with temporal convolutional network and LSTM

    Jing Bi, Xiang Zhang, Haitao Yuan, Jia Zhang, and MengChu Zhou. A hybrid prediction method for realistic network traffic with temporal convolutional network and LSTM . IEEE Trans. Autom. Sci. Eng. , 19(3):1869--1879, July 2022

    work page 2022

  8. [8]

    Informer: Beyond efficient Transformer for long sequence time-series forecasting

    Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. Informer: Beyond efficient Transformer for long sequence time-series forecasting. In Proc. AAAI Conf. Artif. Intell. (AAAI) , volume 35, pages 11106--11115, May 2021

    work page 2021

Show all 56 references
  1. [9]

    FED former: Frequency enhanced decomposed Transformer for long-term series forecasting

    Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. FED former: Frequency enhanced decomposed Transformer for long-term series forecasting. In Proc. Mach. Learn. Res. (ICML) , volume 162, pages 27268--27286, 17--23 Jul 2022

    2022

  2. [10]

    A time series is worth 64 words: Long-term forecasting with Transformers

    Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. A time series is worth 64 words: Long-term forecasting with Transformers . In Proc. Int. Conf. Learn. Represent. (ICLR) , pages 1--24, 2023

    2023

  3. [11]

    Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting

    Yunhao Zhang and Junchi Yan. Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In Int. Conf. Learn. Represent. (ICLR) , pages 1--21, 2023

    2023

  4. [12]

    Jointly modeling spatio–temporal dependencies and daily flow correlations for crowd flow prediction

    Tianzi Zang, Yanmin Zhu, Yanan Xu, and Jiadi Yu. Jointly modeling spatio–temporal dependencies and daily flow correlations for crowd flow prediction. ACM Trans. Knowl. Discov. Data , 15(4), Mar. 2021

    2021

  5. [13]

    Time-wise attention aided convolutional neural network for data-driven cellular traffic prediction

    Wenxin Shen, Haixia Zhang, Shuaishuai Guo, and Chuanting Zhang. Time-wise attention aided convolutional neural network for data-driven cellular traffic prediction. IEEE Wireless Commun. Lett. , 10(8):1747--1751, Aug. 2021

    2021

  6. [14]

    Spatial-temporal aggregation graph convolution network for efficient mobile cellular traffic prediction

    Nan Zhao, Aonan Wu, Yiyang Pei, Ying-Chang Liang, and Dusit Niyato. Spatial-temporal aggregation graph convolution network for efficient mobile cellular traffic prediction. IEEE Commun. Lett. , 26(3):587--591, Mar. 2022

    2022

  7. [15]

    KST-GCN : A knowledge-driven spatial-temporal graph convolutional network for traffic forecasting

    Jiawei Zhu, Xing Han, Hanhan Deng, Chao Tao, Ling Zhao, Pu Wang, Tao Lin, and Haifeng Li. KST-GCN : A knowledge-driven spatial-temporal graph convolutional network for traffic forecasting. IEEE Trans. Intell. Transp. Syst. , 23(9):15055--15065, Sept. 2022

    2022

  8. [16]

    Spatial–temporal graph neural network traffic prediction based load balancing with reinforcement learning in cellular networks

    Shang Liu, Miao He, Zhiqiang Wu, Peng Lu, and Weixi Gu. Spatial–temporal graph neural network traffic prediction based load balancing with reinforcement learning in cellular networks. Inf. Fusion , 103:102079--102095, Mar. 2024

    2024

  9. [17]

    Learning multiaspect traffic couplings by multirelational graph attention networks for traffic prediction

    Jing Huang, Kun Luo, Longbing Cao, Yuanqiao Wen, and Shuyuan Zhong. Learning multiaspect traffic couplings by multirelational graph attention networks for traffic prediction. IEEE Trans. Intell. Transp. Syst. , 23(11):20681--20695, Nov. 2022

    2022

  10. [18]

    Chung-Yi Lin, Hung-Ting Su, Shen-Lung Tung, and Winston H. Hsu. Multivariate and propagation graph attention network for spatial-temporal prediction with outdoor cellular traffic. In Proc. Int. Conf. Inf. Knowledge Manage. (CIKM) , CIKM '21, pages 3248--3252, 2021

    2021

  11. [19]

    Spatial-temporal cellular traffic prediction for 5G and beyond: A graph neural networks-based approach

    Zi Wang, Jia Hu, Geyong Min, Zhiwei Zhao, Zheng Chang, and Zhe Wang. Spatial-temporal cellular traffic prediction for 5G and beyond: A graph neural networks-based approach. IEEE Trans. Ind. Inf. , 19(4):5722--5731, Apr. 2023

    2023

  12. [20]

    A cellular traffic prediction method based on diffusion convolutional GRU and multi-head attention mechanism

    Junbi Xiao, Yunhuan Cong, Wenjing Zhang, and Wenchao Weng. A cellular traffic prediction method based on diffusion convolutional GRU and multi-head attention mechanism. Cluster Comput. , 28(125):1--17, 2025

    2025

  13. [21]

    Multi-stream concept drift self-adaptation using graph neural network

    Ming Zhou, Jie Lu, Yiliao Song, and Guangquan Zhang. Multi-stream concept drift self-adaptation using graph neural network. IEEE Trans. Knowl. Data Eng. , 35(12):12828--12841, Dec. 2023

    2023

  14. [22]

    Addressing concept shift in online time series forecasting: Detect-then-adapt

    YiFan Zhang, Weiqi Chen, Zhaoyang Zhu, Dalin Qin, Liang Sun, Xue Wang, Qingsong Wen, Zhang Zhang, Liang Wang, and Rong Jin. Addressing concept shift in online time series forecasting: Detect-then-adapt. arXiv preprint arXiv:2403.14949 , 2024

    2024 arXiv

  15. [23]

    A unified replay-based continuous learning framework for spatio-temporal prediction on streaming data

    Hao Miao, Yan Zhao, Chenjuan Guo, Bin Yang, Kai Zheng, Feiteng Huang, Jiandong Xie, and Christian S Jensen. A unified replay-based continuous learning framework for spatio-temporal prediction on streaming data. arXiv preprint arXiv:2404.14999 , 2024

    2024 arXiv

  16. [24]

    On tiny episodic memories in continual learning

    Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc'Aurelio Ranzato. On tiny episodic memories in continual learning. arXiv preprint arXiv:1902.10486 , 2019

    1902 arXiv

  17. [25]

    Dark experience for general continual learning: A strong, simple baseline

    Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara. Dark experience for general continual learning: A strong, simple baseline. In Adv. neural inf. proces. syst. (NeurIPS) , volume 33, pages 15920--15930, 2020

    2020

  18. [26]

    TrafficStream : A streaming traffic flow forecasting framework based on graph neural networks and continual learning

    Xu Chen, Junshan Wang, and Kunqing Xie. TrafficStream : A streaming traffic flow forecasting framework based on graph neural networks and continual learning. In Proc. Int. Joint Conf. Artif. Intell. (IJCAI) , pages 3620--3626, 8 2021. Main Track

    2021

  19. [27]

    Multi-agent deep reinforcement learning-based task scheduling and resource sharing for O-RAN -empowered multi- UAV -assisted wireless sensor networks

    Mesfin Leranso Betalo, Supeng Leng, Hayla Nahom Abishu, Fayaz Ali Dharejo, Abegaz Mohammed Seid, Aiman Erbad, Rizwan Ali Naqvi, Longyu Zhou, and Mohsen Guizani. Multi-agent deep reinforcement learning-based task scheduling and resource sharing for O-RAN -empowered multi- UAV -...

    2024

  20. [28]

    Multi-agent DRL -based energy harvesting for freshness of data in UAV -assisted wireless sensor networks

    Mesfin Leranso Betalo, Supeng Leng, Hayla Nahom Abishu, Abegaz Mohammed Seid, Maged Fakirah, Aiman Erbad, and Mohsen Guizani. Multi-agent DRL -based energy harvesting for freshness of data in UAV -assisted wireless sensor networks. IEEE Trans. Netw. Serv. Manage. , 21(6):6527-...

    2024

  21. [29]

    Koudouridis, and Per Tengkvist

    Qing He, Arash Moayyedi, György Dán, Georgios P. Koudouridis, and Per Tengkvist. A meta-learning scheme for adaptive short-term network traffic prediction. IEEE J. Sel. Areas Commun. , 38(10):2271--2283, Oct. 2020

    2020

  22. [30]

    Data-augmentation-based cellular traffic prediction in edge-computing-enabled smart city

    Zi Wang, Jia Hu, Geyong Min, Zhiwei Zhao, and Jin Wang. Data-augmentation-based cellular traffic prediction in edge-computing-enabled smart city. IEEE Trans. Ind. Inf. , 17(6):4179--4187, June 2021

    2021

  23. [31]

    Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data

    Chuanting Zhang, Haixia Zhang, Jingping Qiao, Dongfeng Yuan, and Minggao Zhang. Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data. IEEE J. Sel. Areas Commun. , 37(6):1389--1401, June 2019

    2019

  24. [32]

    STEP : A spatio-temporal fine-granular user traffic prediction system for cellular networks

    Lixing Yu, Ming Li, Wenqiang Jin, Yifan Guo, Qianlong Wang, Feng Yan, and Pan Li. STEP : A spatio-temporal fine-granular user traffic prediction system for cellular networks. IEEE Trans. Mob. Comput. , 20(12):3453--3466, Dec. 2021

    2021

  25. [33]

    Graph attention spatial-temporal network with collaborative global-local learning for citywide mobile traffic prediction

    Kaiwen He, Xu Chen, Qiong Wu, Shuai Yu, and Zhi Zhou. Graph attention spatial-temporal network with collaborative global-local learning for citywide mobile traffic prediction. IEEE Trans. Mob. Comput. , 21(4):1244--1256, Apr. 2022

    2022

  26. [34]

    Lui, and Xiaohong Guan

    Feiyang Sun, Pinghui Wang, Junzhou Zhao, Nuo Xu, Juxiang Zeng, Jing Tao, Kaikai Song, Chao Deng, John C.S. Lui, and Xiaohong Guan. Mobile data traffic prediction by exploiting time-evolving user mobility patterns. IEEE Trans. Mob. Comput. , 21(12):4456--4470, Dec. 2022

    2022

  27. [35]

    Spatio-temporal parallel transformer based model for traffic prediction

    Rahul Kumar, Jo\ a o Mendes-Moreira, and Joydeep Chandra. Spatio-temporal parallel transformer based model for traffic prediction. ACM Trans. Knowl. Discov. Data , 18(9), November 2024

    2024

  28. [36]

    Adaptive graph spatial-temporal Transformer network for traffic forecasting

    Aosong Feng and Leandros Tassiulas. Adaptive graph spatial-temporal Transformer network for traffic forecasting. In Proc. Int. Conf. Inf. Knowledge Manage. (CIKM) , page 3933–3937, 2022

    2022

  29. [37]

    FGITrans : Cross-city Transformer for fine-grained urban flow inference

    Yuhao Zheng, Yishuo Cai, Zihao Cai, Changjun Fan, Senzhang Wang, and Jianxin Wang. FGITrans : Cross-city Transformer for fine-grained urban flow inference. In Proc. Int. Conf. Inf. Knowledge Manage. (CIKM) , page 3415–3423, 2024

    2024

  30. [38]

    Yihong Ma, Patrick Gerard, Yijun Tian, Zhichun Guo, and Nitesh V. Chawla. Hierarchical spatio-temporal graph neural networks for pandemic forecasting. In Proc. Int. Conf. Inf. Knowledge Manage. (CIKM) , pages 1481--1490, 2022

    2022

  31. [39]

    Multi-behavior sequential recommendation with temporal graph transformer

    Lianghao Xia, Chao Huang, Yong Xu, and Jian Pei. Multi-behavior sequential recommendation with temporal graph transformer. IEEE Trans. Knowl. Data Eng. , 35(6):6099--6112, June 2023

    2023

  32. [40]

    Spatial-temporal attention-convolution network for citywide cellular traffic prediction

    Nan Zhao, Zhiyang Ye, Yiyang Pei, Ying-Chang Liang, and Dusit Niyato. Spatial-temporal attention-convolution network for citywide cellular traffic prediction. IEEE Commun. Lett. , 24(11):2532--2536, Nov. 2020

    2020

  33. [41]

    ST-Tran : Spatial-temporal Transformer for cellular traffic prediction

    Qingyao Liu, Jianwu Li, and Zhaoming Lu. ST-Tran : Spatial-temporal Transformer for cellular traffic prediction. IEEE Commun. Lett. , 25(10):3325--3329, Oct. 2021

    2021

  34. [42]

    MVSTGN : A multi-view spatial-temporal graph network for cellular traffic prediction

    Yang Yao, Bo Gu, Zhou Su, and Mohsen Guizani. MVSTGN : A multi-view spatial-temporal graph network for cellular traffic prediction. IEEE Trans. Mob. Comput. , 22(5):2837--2849, May 2023

    2023

  35. [43]

    A spatial-temporal Transformer network for city-level cellular traffic analysis and prediction

    Bo Gu, Junhui Zhan, Shimin Gong, Wanquan Liu, Zhou Su, and Mohsen Guizani. A spatial-temporal Transformer network for city-level cellular traffic analysis and prediction. IEEE Trans. Wireless Commun. , 22(12):9412--9423, Dec. 2023

    2023

  36. [44]

    STMGF : An effective spatial-temporal multi-granularity framework for traffic forecasting

    Zhengyang Zhao, Haitao Yuan, Nan Jiang, Minxiao Chen, Ning Liu, and Zengxiang Li. STMGF : An effective spatial-temporal multi-granularity framework for traffic forecasting. In Proc. Int. Conf. Database Syst. Adv. Appl. (DASFAA) , pages 235--245, 2024

    2024

  37. [45]

    Dynamic multi-granularity spatial-temporal graph attention network for traffic forecasting

    Wei Sang, Huiliang Zhang, Xianchang Kang, Ping Nie, Xin Meng, Benoit Boulet, and Pei Sun. Dynamic multi-granularity spatial-temporal graph attention network for traffic forecasting. Inf. Sci. , 662:120230--120245, Mar. 2024

    2024

  38. [46]

    Lifelong online learning from accumulated knowledge

    Changjian Shui, William Wang, Ihsen Hedhli, Chi Man Wong, Feng Wan, Boyu Wang, and Christian Gagn\' e . Lifelong online learning from accumulated knowledge. ACM Trans. Knowl. Discov. Data , 17(4), Feb. 2023

    2023

  39. [47]

    Online continual learning with maximal interfered retrieval

    Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, and Lucas Page-Caccia. Online continual learning with maximal interfered retrieval. In Adv. neural inf. proces. syst. (NeurIPS) , volume 32, pages 11849--11860, 2019

    2019

  40. [48]

    OneNet : Enhancing time series forecasting models under concept drift by online ensembling

    Yifan Zhang, Qingsong Wen, Xue Wang, Weiqi Chen, Liang Sun, Zhang Zhang, Liang Wang, Rong Jin, and Tieniu Tan. OneNet : Enhancing time series forecasting models under concept drift by online ensembling. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, ed...

    2023

  41. [49]

    LNTP : An end-to-end online prediction model for network traffic

    Lianming Zhang, Huan Zhang, Qian Tang, Pingping Dong, Zhen Zhao, Yehua Wei, Jing Mei, and Kaiping Xue. LNTP : An end-to-end online prediction model for network traffic. IEEE Network , 35(1):226--233, Jan./Feb. 2021

    2021

  42. [50]

    A multi-source dataset of urban life in the city of Milan and the province of Trentino

    Gianni Barlacchi, Marco De Nadai, Roberto Larcher, Antonio Casella, Cristiana Chitic, Giovanni Torrisi, Fabrizio Antonelli, Alessandro Vespignani, Alex Pentland, and Bruno Lepri. A multi-source dataset of urban life in the city of Milan and the province of Trentino . Sci. Data...

    2015

  43. [51]

    Dynamic modification neural network model for short-term traffic prediction

    Da Guo, Xingwen Xia, Lin Zhu, and Yong Zhang. Dynamic modification neural network model for short-term traffic prediction. Procedia Comput. Sci. , 187:134--139, June 2021

    2021

  44. [52]

    A novel short-term traffic prediction model based on SVD and ARIMA with blockchain in industrial Internet of things

    Ying Miao, Xiuhong Bai, Yuxuan Cao, Yuwen Liu, Fei Dai, Fan Wang, Lianyong Qi, and Wanchun Dou. A novel short-term traffic prediction model based on SVD and ARIMA with blockchain in industrial Internet of things. IEEE Internet Things J. , 10(24):21217--21226, Dec. 2023

    2023

  45. [53]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Adv. neural inf. proces. syst. (NeurIPS) , volume 30, pages 1--11. Curran Associates, Inc., 2017

    2017

  46. [54]

    Adaptive hybrid spatial-temporal graph neural network for cellular traffic prediction

    Xing Wang, Kexin Yang, Zhendong Wang, Junlan Feng, Lin Zhu, Juan Zhao, and Chao Deng. Adaptive hybrid spatial-temporal graph neural network for cellular traffic prediction. In Proc. IEEE Int. Conf. Commun. (ICC) , pages 4026--4032, 2023

    2023

  47. [55]

    Towards dynamic spatial-temporal graph learning: A decoupled perspective

    Binwu Wang, Pengkun Wang, Yudong Zhang, Xu Wang, Zhengyang Zhou, Lei Bai, and Yang Wang. Towards dynamic spatial-temporal graph learning: A decoupled perspective. In Proc. AAAI Conf. Artif. Intell. , volume 38, pages 9089--9097, Mar. 2024

    2024

  48. [56]

    Machine learning for 6G enhanced ultra-reliable and low-latency services

    Yan Liu, Yansha Deng, Arumugam Nallanathan, and Jinhong Yuan. Machine learning for 6G enhanced ultra-reliable and low-latency services. IEEE Wireless Commun. , 30(2):48--54, Apr. 2023

    2023

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Reviewed August 6, 2026 · model on record in the stance chip above.