Granger causal representation learning for groups of time series  

作　　者：Ruichu CAI Yunjin WU Xiaokai HUANG Wei CHEN Tom Z.J.FU Zhifeng HAO 

机构地区：[1]School of Computer Science,Guangdong University of Technology,Guangzhou 510006,China [2]Peng Cheng Laboratory,Shenzhen 518066,China [3]College of Science,Shantou University,Shantou 515063,China

出　　处：《Science China(Information Sciences)》2024年第5期52-64,共13页中国科学（信息科学）（英文版）

基　　金：supported in part by National Key R&D Program of China(Grant No.2021ZD0111501);National Science Fund for Excellent Young Scholars(Grant No.62122022);National Natural Science Foundation of China(Grant Nos.61876043,61976052,62206064);the Major Key Project of PCL(Grant No.PCL2021A12).

摘　　要：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 discovering 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 temporal 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.

关 键 词：Granger causal discovery Granger causal representation learning time series data recurrent neural network multiset canonical correlation analysis 

分 类 号：O211.61[理学—概率论与数理统计]