基于观测数据的时间序列因果推断综述

曾泽凡; 陈思雅; 龙洗; 金光

doi:10.11959/j.issn.2096-0271.2022059

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基于观测数据的时间序列因果推断综述

研究 | 更新时间：2024-06-03

- 基于观测数据的时间序列因果推断综述
- Overview of observational data-based time series causal inference
- 大数据 2023年9卷第4期页码：139-158
- 作者机构：
  
  1. 国防科技大学系统工程学院，湖南长沙 410073
  2. 国防科技大学空天科学学院，湖南长沙 410073
- 作者简介：
  
  [ "曾泽凡（1993- ），男，国防科技大学系统工程学院硕士生，主要研究方向为数据分析与数据建模" ]
  [ "陈思雅（1998- ），女，国防科技大学系统工程学院博士生，主要研究方向为时间序列异常检测、故障诊断" ]
  [ "龙洗（1999- ），男，国防科技大学空天科学学院博士生，主要研究方向为航天任务规划、因果推断、强化学习" ]
  [ "金光（1973- ）男，博士，国防科技大学系统工程学院研究员，主要研究方向为寿命预测与健康管理、系统试验与评估" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2022059
  中图分类号： TP391
- 网络首发：2023-07，
  
  纸质出版：2023-07-15
- 稿件说明：
移动端阅览
曾泽凡, 陈思雅, 龙洗, 等. 基于观测数据的时间序列因果推断综述[J]. 大数据, 2023,9(4):139-158.

Zefan ZENG, Siya CHEN, Xi LONG, et al. Overview of observational data-based time series causal inference[J]. Big data research, 2023, 9(4): 139-158.
曾泽凡, 陈思雅, 龙洗, 等. 基于观测数据的时间序列因果推断综述[J]. 大数据, 2023,9(4):139-158. DOI： 10.11959/j.issn.2096-0271.2022059.

Zefan ZENG, Siya CHEN, Xi LONG, et al. Overview of observational data-based time series causal inference[J]. Big data research, 2023, 9(4): 139-158. DOI： 10.11959/j.issn.2096-0271.2022059.

摘要

数据存储量的扩大和计算能力的提升为基于观测数据推断时间序列的因果关系开辟了新途径。在时间序列因果推断的基本性质和研究现状的基础上，系统梳理了5种基于观测数据的时间序列因果推断方法，即Granger因果分析方法、基于信息论的方法、因果网络结构学习算法、基于结构因果模型的方法和基于非线性状态空间模型的方法。然后，根据不同应用场景的数据特点，结合方法的功能和适配性，对基于观测数据的时间序列因果推断方法在经济金融、医疗和生物学、地球系统科学和其他工程领域的典型应用进行了简要介绍。最后，结合时间序列因果推断的重难点问题，比较5种方法的优缺点，分析下一步研究重点，展望未来的研究方向。

Abstract

With the increase of data storage and the improvement of computing power

using observational data to infer time series causality has become a novel approach.Based on the properties and research status of time series causal inference

five observational data-based methods were induced

including Granger causal analysis

information theory-based method

causal network structure learning algorithm

structural causal model-based method and method based on nonlinear state-space model.Then we briefly introduced typical applications in economics and finance

medical science and biology

earth system science and other engineering fields.Further

we compared the advantages and disadvantages and analyzed the ways for improvement of the five methods according to the focus and difficulties of time series causal inference.Finally

we looked into the future research directions.

关键词

Keywords

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