MCINet: Multi-Channel Informer Networks for Long Sequence Time-Series Forecasting in Ocean-Going Ship Satellite Communication Networks-Reference-Cited by-同舟云学术

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MCINet: Multi-Channel Informer Networks for Long Sequence Time-Series Forecasting in Ocean-Going Ship Satellite Communication Networks

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:2023 7th International Conference on Transportation Information and Safety (ICTIS)
language:
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Author:

Li Qiuliang¹,Liu Jin¹,Zhi Jiamao²,Gong Peizhu¹,Wang Junxiang³,Wu Zhongdai⁴

Affiliation:

1. Shanghai Maritime University,College of Information Engineering,Shanghai,China

2. China Shipping Telecommunication Co., Ltd. Shanghai Ship and Shipping Research Institute,Shanghai,China

3. National Engineering Research Center of Ship Transportation Control Systems COSCO SHIPPING TECHNOLOGY Co., Ltd,Shanghai,China

4. State Key Laboratory of Maritime Technology and Safety Shanghai Ship and Shipping Research Institute,Shanghai,China

Funder

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10243582/10243583/10243971.pdf?arnumber=10243971

Reference23 articles.

1. Support vector regression for link load prediction

2. An approach towards resource management and transportation of VBR video traffic

3. Multi-Lane Capsule Network for Classifying Images With Complex Background

4. Network traffic prediction of the optimized BP neural network based on Glowworm Swarm Algorithm

5. Prediction of Internet traffic using time series and neural networks[C];katris;Proceedings of international work-conference on time series analysis (ITISE 2014),2014

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