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多元时间序列因果关系分析研究综述

任伟杰 韩敏

任伟杰, 韩敏. 多元时间序列因果关系分析研究综述. 自动化学报, 2021, 47(1): 64−78 doi: 10.16383/j.aas.c180189
引用本文: 任伟杰, 韩敏. 多元时间序列因果关系分析研究综述. 自动化学报, 2021, 47(1): 64−78 doi: 10.16383/j.aas.c180189
Ren Wei-Jie, Han Min. Survey on causality analysis of multivariate time series. Acta Automatica Sinica, 2021, 47(1): 64−78 doi: 10.16383/j.aas.c180189
Citation: Ren Wei-Jie, Han Min. Survey on causality analysis of multivariate time series. Acta Automatica Sinica, 2021, 47(1): 64−78 doi: 10.16383/j.aas.c180189

多元时间序列因果关系分析研究综述

doi: 10.16383/j.aas.c180189
基金项目: 国家自然科学基金(61773087), 中央高校基本科研业务费(DUT18RC(6)005)资助
详细信息
    作者简介:

    任伟杰:大连理工大学电子信息与电气工程学部博士研究生. 主要研究方向为时间序列分析和特征选择.E-mail: renweijie@mail.dlut.edu.cn

    韩敏:大连理工大学电子信息与电气工程学部教授. 主要研究方向为模式识别, 复杂系统建模及时间序列预测. 本文通信作者.E-mail: minhan@dlut.edu.cn

Survey on Causality Analysis of Multivariate Time Series

Funds: Supported by National Natural Science Foundation of China (61773087) and Fundamental Research Funds for the Central Universities (DUT18RC(6)005)
  • 摘要:

    多元时间序列的因果关系分析是数据挖掘领域的研究热点. 时间序列数据包含着与时间动态有关的、未知的、有价值的信息, 因此若能挖掘出这些知识进而对时间序列未来趋势进行预测或干预, 具有重要的现实意义. 为此, 本文综述了多元时间序列因果关系分析的研究进展、应用与展望. 首先, 本文归纳了主要的因果分析方法, 包括Granger因果关系分析、基于信息理论的因果分析和基于状态空间的因果分析; 然后, 总结了不同方法的优缺点、适用范围和发展方向, 并概述了其在不同领域的典型应用; 最后, 讨论了多元时间序列因果分析方法待解决的问题和未来研究趋势.

  • 图  1  收敛交叉映射基本原理示意图

    Fig.  1  Schematic diagram of the basic principle of convergence cross mapping

    表  1  Granger因果关系分析及其改进方法

    Table  1  Granger causality analysis and its improvement methods

    类别 研究者 发表年份 方法名称 文献
    Granger因果模型 Granger 1969 Granger 因果指数 (GCI) [15]
    条件Granger因果模型 Geweke 1982 条件 Granger 因果指数 (CGCI) [23]
    Chen 等 2004 条件扩展 Granger 因果指数 (CEGCI) [24]
    Siggiridou 等 2016 限制条件 Granger 因果指数 (RCGCI) [25]
    Lasso-Granger因果模型 Arnold 等 2007 Lasso-Granger 因果模型 [26]
    Shojaie 等 2010 截断 Lasso-Granger 因果模型 [27]
    Bolstad 等 2011 Grouped-Lasso-Granger 因果模型 [28]
    Yang 等 2017 Grouped-Lasso 非线性条件 Granger 因果模型 [29]
    非线性Granger因果模型 Ancona 等 2004 RBF-Granger 因果模型 [30]
    Marinazzo 等 2008 Kernel-Granger 因果模型 [31-32]
    Wu 等 2011 KCCA-Granger 因果模型 [33]
    Hu 等 2014 Copula-Granger 因果模型 [34]
    Montalto 等 2015 NN-Granger 因果模型 [35]
    频域Granger因果模型 Geweke 1982 Spectral-Granger 因果模型 [23]
    Baccalá 等 2001 偏定向相干性 (PDC) [36]
    Kamiński 等 2001 直接传递函数 (DTF) [37]
    下载: 导出CSV

    表  2  基于信息理论的因果关系分析方法

    Table  2  Causality analysis methods based on information theory

    类别 研究者 发表年份 方法名称 文献
    转移熵 Schreiber 2000 转移熵 (TE) [40]
    Staniek 等 2008 符号转移熵 (STE) [42]
    Kugiumtzis 2013 偏符号转移熵 (PSTE) [43]
    条件熵 Faes 等 2011 条件熵 (CE) [44]
    条件互信息 Frenzel 等 2007 偏互信息 (PMI) [45]
    Kugiumtzis 2013 基于混合嵌入的偏互信息 (PMIME) [46]
    下载: 导出CSV

    表  3  因果分析方法应用范围比较

    Table  3  Comparison of application range of causality analysis methods

    研究者 方法名称 非线性 多变量 非平稳 文献
    Granger Granger 因果指数 [15]
    Geweke 条件 Granger 因果指数 [23]
    Chen 等 条件扩展 Granger 因果指数 [24]
    Siggiridou 等 限制条件 Granger 因果指数 [25]
    Arnold 等 Lasso-Granger 因果模型 [26]
    Shojaie 等 截断 Lasso-Granger 因果模型 [27]
    Bolstad 等 Grouped-Lasso-Granger 因果模型 [28]
    Yang 等 Grouped-Lasso 非线性条件 Granger 因果模型 [29]
    Ancona 等 RBF-Granger 因果模型 [30]
    Marinazzo 等 Kernel-Granger 因果模型 [31-32]
    Wu 等 KCCA-Granger 因果模型 [33]
    Hu 等 Copula-Granger 因果模型 [34]
    Montalto 等 NN-Granger 因果模型 [35]
    Geweke Spectral-Granger 因果模型 [23]
    Baccalá 等 偏定向相干性 [36]
    Kamiński 等 直接传递函数 [37]
    Schreiber 转移熵 [40]
    Staniek 等 符号转移熵 [42]
    Kugiumtzis 偏符号转移熵 [43]
    Faes 等 条件熵 [44]
    Frenzel 等 偏互信息 [45]
    Kugiumtzis 基于混合嵌入的偏互信息 [46]
    Arnhold 等 非线性相互依赖指标 SH [61]
    Quiroga 等 非线性相互依赖指标 N [62]
    Andrzejak 等 非线性相互依赖指标 M [63]
    Chicharro 等 非线性相互依赖指标 L [64]
    Sugihara 等 收敛交叉映射 [65]
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-04-02
  • 录用日期:  2018-11-22
  • 网络出版日期:  2021-01-29
  • 刊出日期:  2021-01-29

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