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Granger causal representation learning for groups of time series

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Discovering causality from multivariate time series is an important but challenging problem.Most existing methods focus on estimating the Granger causal structures among multivariate time series,while ignoring the prior knowledge of these time series,e.g.,the group of the time series.Focusing on discov-ering the Granger causal structures among groups of time series,we propose a Granger causal representation learning method to solve this problem.First,we use the multiset canonical correlation analysis method to learn the Granger causal representation of each group of time series.Then,we model the Granger causal relationships among the learned Granger causal representations using a recurrent neural network with tem-poral information.Finally,we formulate the above two stages into one unified optimization problem,which is efficiently solved using the augmented Lagrangian method.We conduct extensive experiments on synthetic and real-world datasets to validate the correctness and effectiveness of the proposed method.

Granger causal discoveryGranger causal representation learningtime series datarecurrent neural networkmultiset canonical correlation analysis

Ruichu CAI、Yunjin WU、Xiaokai HUANG、Wei CHEN、Tom Z.J.FU、Zhifeng HAO

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School of Computer Science,Guangdong University of Technology,Guangzhou 510006,China

Peng Cheng Laboratory,Shenzhen 518066,China

College of Science,Shantou University,Shantou 515063,China

National Key R&D Program of ChinaNational Science Fund for Excellent Young ScholarsNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaMajor Key Project of PCL

2021ZD011150162122022618760436197605262206064PCL2021A12

2024

10.1007/s11432-021-3724-0

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2024.67(5)
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