CGF: A Category Guidance Based PM$_{2.5}$ Sequence Forecasting Training Framework-Reference-Cited by-同舟云学术

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CGF: A Category Guidance Based PM$_{2.5}$ Sequence Forecasting Training Framework

Published:2023-10-01 Issue:10 Volume:35 Page:10125-10139
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Yu Haomin¹^ORCID,Hu Jilin¹^ORCID,Zhou Xinyuan²,Guo Chenjuan³^ORCID,Yang Bin³^ORCID,Li Qingyong²^ORCID

Affiliation:

1. Department of Computer Science, Aalborg University, Aalborg, Denmark

2. Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Beijing Jiaotong University, Beijing, China

3. East China Normal University, Shanghai, China

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Wuyi University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10251471/10064188.pdf?arnumber=10064188

Reference39 articles.

1. Long Short-Term Memory

2. Global models for time series forecasting: A Simulation study

3. A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory

4. Toward automatic time-series forecasting using neural networks;yan;IEEE Trans Neural Netw Learn Syst,2012

5. Attention is all you need;vaswani;Proc Adv Neural Inf Process Syst,2017

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. AutoCTS++: zero-shot joint neural architecture and hyperparameter search for correlated time series forecasting;The VLDB Journal;2024-07-30

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