[1]武欣茹,李爱萍,段利国.一种考虑节假日影响的时空网络流量预测方法[J].郑州大学学报(工学版),2026,47(3):117-125.[doi:10.13705/j.issn.1671-6833.2025.06.007]
　WU Xinru,LI Aiping,DUAN Liguo.A Spatial-temporal Network Traffic Prediction Method Considering the Impact of Holidays[J].Journal of Zhengzhou University (Engineering Science),2026,47(3):117-125.[doi:10.13705/j.issn.1671-6833.2025.06.007]
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一种考虑节假日影响的时空网络流量预测方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年3期
页码:
117-125
栏目:
出版日期:
2026-05-27

文章信息/Info

Title:
A Spatial-temporal Network Traffic Prediction Method Considering the Impact of Holidays
文章编号:
1671-6833(2026)03-0117-09
作者:
武欣茹1, 李爱萍1, 段利国1,2
1.太原理工大学 计算机科学与技术学院(大数据学院),山西 太原 030024;2.山西电子科技学院,山西 临汾 041000
Author(s):
WU Xinru1, LI Aiping1, DUAN Liguo1,2
1.College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; 2.Shanxi University of Electronic Science and Technology,Linfen 041000,China
关键词:
网络流量预测;  时空数据;  节假日特征;  神经网络;  深度学习
Keywords:
network traffic prediction;  spatio-temporal data;  holiday characteristics;  neural network;  deep learning
分类号:
TN929.53:TP183
DOI:
10.13705/j.issn.1671-6833.2025.06.007
文献标志码:
A
摘要:
现有深度学习方法在网络流量预测时没有充分考虑节假日的影响,针对此,在分析历史数据对预测性能影响的基础上,提出了一种考虑节假日影响的时空网络流量预测方法。首先,提出一种考虑节假日这一外部因素的生成历史数据的方法,使得历史数据中的节假日这一语义特征对网络流量预测产生作用;其次,通过时空学习块来学习网络流量的时间相关性、复杂空间相关性以及时空异构性,获得历史数据包含的时空综合特征;最后,通过跳跃连接融合多个块的结果,输出最后的预测结果。在意大利米兰数据集和中国台湾数据集上的实验结果表明:与最近的同类模型AHSTGNN相比,所提模型在意大利米兰数据集和中国台湾数据集上的MAE分别降低了0.9和24.35,RMSE分别降低了1.81和58.25,说明了该方案的有效性。
Abstract:
Existing deep learning methods could not fully consider the impact of holidays when predicting network traffic. To address this issue, based on an analysis of the influence of historical data on prediction performance, a spatio-temporal network traffic prediction method that was proposed to account for the impact of holidays. Firstly, a method for generating historical data that incorporates holidays as an external factor was introduced, enabling the semantic feature of holidays within the historical data to play a role in network traffic prediction. Secondly, spatiotemporal learning blocks were employed to capture the temporal correlations, complex spatial correlations, and spatiotemporal heterogeneity of network traffic, thereby obtaining the comprehensive spatio-temporal features embedded in the historical data. Finally, the results from multiple blocks were fused through skip connections to output the final prediction results. Experimental results on the Milan (Italy) and Taiwan (China) datasets demonstrated that, compared with the recent similar model AHSTGNN, the proposed model reduced the MAE by 0.9 and 24.35 on the two datasets, respectively, and decreased the RMSE by 1.81 and 58.25, respectively. This illustrated the effectiveness of the proposed approach.

参考文献/References:

[1]陈浩杰, 黄锦, 左兴权, 等. 基于宽度&深度学习的基站网络流量预测方法[J]. 郑州大学学报(工学版), 2022, 43(1): 7-13.

CHEN H J, HUANG J, ZUO X Q, et al. Base station network traffic prediction method based on wide & deep learning[J]. Journal of Zhengzhou University (Engineering Science), 2022, 43(1): 7-13.
[2]WANG D, LIU Y J, SONG B. A credible traffic prediction method based on self-supervised causal discovery[J]. Science China Information Sciences, 2024, 67(5): 152303.
[3]QIN X, ZHU W W, HU Q, et al. DenseNet-Transformer: a deep learning method for spatial-temporal traffic prediction in optical fronthaul network[J]. Computer Networks, 2024, 253: 110674.
[4]YU B, YIN H T, ZHU Z X. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting[C]∥Proceedings of the 27th International Joint Conference on Artificial Intelligence. New York: ACM, 2018: 3634-3640.
[5]ZHANG C Y, PATRAS P. Long-term mobile traffic forecasting using deep spatio-temporal neural networks[C]∥Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing. New York:ACM, 2018: 231-240.
[6]LAN S Y, MA Y T, HUANG W K, et al. DSTAGNN: dynamic spatial-temporal aware graph neural network for traffic flow forecasting[C]∥Proceedings of the 39th International Conference on Machine Learning.Baltimore:PMLR,2022: 11906-11917.
[7]WU Z H, PAN S R, LONG G D, et al. Graph WaveNet for deep spatial-temporal graph modeling[C]∥Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Macao: International Joint Conferences on Artificial Intelligence Organization, 2019: 1907-1913.
[8]WANG X, YANG K X, WANG Z D, et al. Adaptive hybrid spatial-temporal graph neural network for cellular traffic prediction[C]∥ICC 2023-IEEE International Conference on Communications. Piscataway:IEEE, 2023: 4026-4032.
[9]GUO K, HU Y L, QIAN Z, et al. Dynamic graph convolution network for traffic forecasting based on latent network of Laplace matrix estimation[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(2): 1009-1018.
[10] JIN Z L, QIAN J, KONG Z X, et al. A mobility aware network traffic prediction model based on dynamic graph attention spatio-temporal network[J]. Computer Networks, 2023, 235: 109981.
[11] HE K, VU T X, CHATZINOTAS S, et al.Spatio-temporal traffic prediction using crossover attention for communications and networking[C]∥2024 IEEE Globecom Workshops (GC Wkshps). Piscataway:IEEE, 2024: 1-6.
[12] RAO Z H, XU Y Y, PAN S M, et al. Cellular traffic prediction: a deep learning method considering dynamic nonlocal spatial correlation, self-attention, and correlation of spatiotemporal feature fusion[J]. IEEE Transactions on Network and Service Management, 2023, 20(1): 426-440.
[13] SONG C, LIN Y F, GUO S N, et al. Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting[C]∥Proceedings of the AAAI Conference on Artificial Intelligence. New York: AAAI Press, 2020: 914-921.
[14] GUO K, HU Y L, SUN Y F, et al. Hierarchical graph convolution network for traffic forecasting[C]∥Proceedings of the AAAI Conference on Artificial Intelligence. New York: AAAI Press, 2021: 151-159.
[15]张翔. 基于ST-LSTM神经网络的网络流量预测方法研究[D]. 北京: 北京工业大学, 2021.
ZHANG X. Research on network traffic prediction method based on ST-LSTM neural network[D]. Beijing: Beijing University of Technology, 2021.
[16]徐冰冰, 岑科廷, 黄俊杰, 等. 图卷积神经网络综述[J]. 计算机学报, 2020, 43(5):755-780.
XU B B, CEN K T, HUANG J J, et al. Survey of graph convolutional neural networks[J]. Chinese Journal of Computers, 2020, 43(5): 755-780.
[17] LEI B, YAO L N, LI C, et al.Adaptive graph convolutional recurrent network for traffic forecasting[EB/OL].(2020-10-22)[2025-02-10]. https:∥arxiv. org/abs/2007.02842.
[18] VELICKOVIC P, CUCURULL G, CASANOVA A,et al.Graph attention networks[EB/OL]. (2018-02-04)[2025-02-11]. https:∥arxiv.org/abs/1710.10903.
[19]康梦轩, 宋俊平, 范鹏飞, 等. 基于深度学习的网络流量预测研究综述[J]. 计算机工程与应用, 2021, 57(10): 1-9.
KANG M X, SONG J P, FAN P F, et al. Survey of network traffic forecast based on deep learning[J]. Computer Engineering and Applications, 2021, 57(10): 1-9.
[20] LIN C Y, SU H T, TUNG S L, et al. Multivariate and propagation graph attention network for spatial-temporal prediction with outdoor cellular traffic[C]∥Proceedings of the 30th ACM International Conference on Information & Knowledge Management. New York: ACM, 2021: 3248-3252.
[21] HE K W, CHEN X, WU Q, et al. Graph attention spatial-temporal network with collaborative global-local learning for citywide mobile traffic prediction[J]. IEEE Transactions on Mobile Computing, 2022, 21(4): 1244-1256.
[22]于江燕,王倩,孟宪静,等. 基于动态时空Transformer的城市网络流量预测方法[J]. 计算机工程与应用, 2025, 61(18):290-299.
YU J Y, WANG Q, MENG X J, et al. Urban network traffic forecasting method based on dynamic spatio-temporal Transformer[J]. Computer Engineering and Applications, 2025, 61(18):290-299.
[23]孙博. 基于机器学习的蜂窝网络流量预测及其应用研究[D]. 北京: 北京邮电大学, 2024.
SUN B. Research on cellular network traffic prediction and its application based on machine learning[D]. Beijing: Beijing University of Posts and Telecommunications, 2024.

更新日期/Last Update: 2026-05-27