Causality analysis based on matrix transfer entropy

Rongjin Ma; Badong Chen; Jianfeng Xiao; Jingli Shao

doi:10.1109/MLSP.2018.8517095

Causality analysis based on matrix transfer entropy

Rongjin Ma
, Badong Chen
, Jianfeng Xiao
, Jingli Shao

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University
VICON Technology (Shenzhen) Co. Ltd

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Transfer Entropy (TE) is one of the most commonly used methods to detect the causal relationship between a pair of time series. However, the computational complexity of the TE is very hign, because its calculation needs to estimate the probability distribution of the variables. In order to solve this problem, we propose a new version of the TE based on the concept of Matrix Entropy (MT), called Matrix Transfer Entropy (MTE). MTE can be used for two variables with linear or non-linear causal relationships. Compared with the traditional TE, the new approach can achieve more robust results. Bypassing the estimation of the probability density functions (PDFs) of the variables, the computational complexity of the MTE is not high. Experimental results on two toy examples are provided to demonstrate the performance of the MTE. Additionally, the new method is applied to a real clinical dataset to analyze the cardiorespiratory causality.

Original language	English
Title of host publication	2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings
Editors	Nelly Pustelnik, Zheng-Hua Tan, Zhanyu Ma, Jan Larsen
Publisher	IEEE Computer Society
ISBN (Electronic)	9781538654774
DOIs	https://doi.org/10.1109/MLSP.2018.8517095
State	Published - 31 Oct 2018
Event	28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Aalborg, Denmark Duration: 17 Sep 2018 → 20 Sep 2018

Publication series

Name	IEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume	2018-September
ISSN (Print)	2161-0363
ISSN (Electronic)	2161-0371

Conference

Conference	28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018
Country/Territory	Denmark
City	Aalborg
Period	17/09/18 → 20/09/18

Keywords

Causality
Computational complexity
Matrix Entropy
Matrix Transfer Entropy
Transfer Entropy

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10.1109/MLSP.2018.8517095

Cite this

Ma, R., Chen, B., Xiao, J., & Shao, J. (2018). Causality analysis based on matrix transfer entropy. In N. Pustelnik, Z.-H. Tan, Z. Ma, & J. Larsen (Eds.), 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings Article 8517095 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2018-September). IEEE Computer Society. https://doi.org/10.1109/MLSP.2018.8517095

Ma, Rongjin ; Chen, Badong ; Xiao, Jianfeng et al. / Causality analysis based on matrix transfer entropy. 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings. editor / Nelly Pustelnik ; Zheng-Hua Tan ; Zhanyu Ma ; Jan Larsen. IEEE Computer Society, 2018. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP).

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title = "Causality analysis based on matrix transfer entropy",

abstract = "Transfer Entropy (TE) is one of the most commonly used methods to detect the causal relationship between a pair of time series. However, the computational complexity of the TE is very hign, because its calculation needs to estimate the probability distribution of the variables. In order to solve this problem, we propose a new version of the TE based on the concept of Matrix Entropy (MT), called Matrix Transfer Entropy (MTE). MTE can be used for two variables with linear or non-linear causal relationships. Compared with the traditional TE, the new approach can achieve more robust results. Bypassing the estimation of the probability density functions (PDFs) of the variables, the computational complexity of the MTE is not high. Experimental results on two toy examples are provided to demonstrate the performance of the MTE. Additionally, the new method is applied to a real clinical dataset to analyze the cardiorespiratory causality.",

keywords = "Causality, Computational complexity, Matrix Entropy, Matrix Transfer Entropy, Transfer Entropy",

author = "Rongjin Ma and Badong Chen and Jianfeng Xiao and Jingli Shao",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 ; Conference date: 17-09-2018 Through 20-09-2018",

year = "2018",

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language = "英语",

series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",

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Ma, R, Chen, B, Xiao, J & Shao, J 2018, Causality analysis based on matrix transfer entropy. in N Pustelnik, Z-H Tan, Z Ma & J Larsen (eds), 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings., 8517095, IEEE International Workshop on Machine Learning for Signal Processing, MLSP, vol. 2018-September, IEEE Computer Society, 28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018, Aalborg, Denmark, 17/09/18. https://doi.org/10.1109/MLSP.2018.8517095

Causality analysis based on matrix transfer entropy. / Ma, Rongjin; Chen, Badong; Xiao, Jianfeng et al.
2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings. ed. / Nelly Pustelnik; Zheng-Hua Tan; Zhanyu Ma; Jan Larsen. IEEE Computer Society, 2018. 8517095 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2018-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Causality analysis based on matrix transfer entropy

AU - Ma, Rongjin

AU - Chen, Badong

AU - Xiao, Jianfeng

AU - Shao, Jingli

PY - 2018/10/31

Y1 - 2018/10/31

N2 - Transfer Entropy (TE) is one of the most commonly used methods to detect the causal relationship between a pair of time series. However, the computational complexity of the TE is very hign, because its calculation needs to estimate the probability distribution of the variables. In order to solve this problem, we propose a new version of the TE based on the concept of Matrix Entropy (MT), called Matrix Transfer Entropy (MTE). MTE can be used for two variables with linear or non-linear causal relationships. Compared with the traditional TE, the new approach can achieve more robust results. Bypassing the estimation of the probability density functions (PDFs) of the variables, the computational complexity of the MTE is not high. Experimental results on two toy examples are provided to demonstrate the performance of the MTE. Additionally, the new method is applied to a real clinical dataset to analyze the cardiorespiratory causality.

AB - Transfer Entropy (TE) is one of the most commonly used methods to detect the causal relationship between a pair of time series. However, the computational complexity of the TE is very hign, because its calculation needs to estimate the probability distribution of the variables. In order to solve this problem, we propose a new version of the TE based on the concept of Matrix Entropy (MT), called Matrix Transfer Entropy (MTE). MTE can be used for two variables with linear or non-linear causal relationships. Compared with the traditional TE, the new approach can achieve more robust results. Bypassing the estimation of the probability density functions (PDFs) of the variables, the computational complexity of the MTE is not high. Experimental results on two toy examples are provided to demonstrate the performance of the MTE. Additionally, the new method is applied to a real clinical dataset to analyze the cardiorespiratory causality.

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KW - Computational complexity

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Ma R, Chen B, Xiao J, Shao J. Causality analysis based on matrix transfer entropy. In Pustelnik N, Tan ZH, Ma Z, Larsen J, editors, 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings. IEEE Computer Society. 2018. 8517095. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). doi: 10.1109/MLSP.2018.8517095

Causality analysis based on matrix transfer entropy

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