Dynamic Personalized Graph Neural Ordinary Differential Equation Network for Multivariate Time Series Prediction of Chemical Processes-Reference-Cited by-同舟云学术

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Dynamic Personalized Graph Neural Ordinary Differential Equation Network for Multivariate Time Series Prediction of Chemical Processes

Published:2024-05-17 Issue: Volume: Page:2044-2049
ISSN:
Container-title:2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)
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Author:

Wang Nan¹,Cao Yanming¹,Wang Lei²,Luo Fei¹,Li Xueming³,Han Yongming³,Hu Xuan³

Affiliation:

1. CNPC PetroChina Planning and Engineering Institute,Key Laboratory of Oil & Gas Business Chain Optimization,Beijing,China

2. PetroChina Dalian Petrochemical Company,Liaoning,China

3. College of Information Science and Technology, Beijing University of Chemical Technology Engineering,Research Center of Intelligent PSE, Ministry of Education in China,Beijing,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10606543/10606546/10606557.pdf?arnumber=10606557

Reference16 articles.

1. Temporal regularized matrix factorization for high-dimensional time series prediction;Yu

2. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling;Bai;arXiv,2018

3. Temporal pattern attention for multivariate time series forecasting

4. Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

5. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

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