M$^{2}$STL: Multi-Range Multi-Level Spatial-Temporal Learning Model for Network Traffic Prediction-Reference-Cited by-同舟云学术

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M$^{2}$STL: Multi-Range Multi-Level Spatial-Temporal Learning Model for Network Traffic Prediction

Published:2024-09 Issue:5 Volume:11 Page:4315-4329
ISSN:2327-4697
Container-title:IEEE Transactions on Network Science and Engineering
language:
Short-container-title:IEEE Trans. Netw. Sci. Eng.

Author:

Xie Ruotian¹^ORCID,Wen Jigang²^ORCID,Chen Xiaodi³^ORCID,Xie Kun¹^ORCID,liang Wei²^ORCID,Xiong Naixue²^ORCID,Zhang Dafang¹,Xie Gaogang⁴^ORCID,Li Kenli¹^ORCID

Affiliation:

1. College of Computer Science, Electronics Engineering, Hunan University, Changsha, China

2. School of Computer Science, Engineering, Hunan University of Science, Technology, Xiangtan, China

3. State Grid Yichang Electric Power Supply Company, Yichang, China

4. Computer Network Information Center, Chinese Academy of Sciences, Beijing, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Hunan Provincial Innovation Foundation for Postgraduate

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6488902/10637788/10568345.pdf?arnumber=10568345

Reference60 articles.

1. Joint Demand Forecasting and Network Slice Pricing for Profit Maximization in Network Slicing

2. A Data-driven Base Station Sleeping Strategy Based on Traffic Prediction

3. Study on network traffic prediction techniques

4. Deep Learning-Powered Vessel Trajectory Prediction for Improving Smart Traffic Services in Maritime Internet of Things

5. TimeAutoAD: Autonomous Anomaly Detection With Self-Supervised Contrastive Loss for Multivariate Time Series

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