Transfer Entropy Estimation via Copula

DOI

10.2991/mecs-17.2017.159How to use a DOI?

Keywords

Time series analysis, Transfer entropy, Copula

Abstract

Transfer entropy provides a powerful information theoretic measurement of directed information flow between time series variables. Effective and convenient methods of estimation are desirable in practice. This article discusses the formulation of how to estimate transfer entropy via the statistical copula. Furthermore, this article provides theoretical justifications, and two estimation approaches via the Gaussian copula transformation and kernel methods. The experiment demonstrates that the proposed estimation approaches are competitive with the Linear estimator and the Nearest Neighbour estimator.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xue Tian
PY  - 2016/06
DA  - 2016/06
TI  - Transfer Entropy Estimation via Copula
BT  - Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
PB  - Atlantis Press
SP  - 356
EP  - 360
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecs-17.2017.159
DO  - 10.2991/mecs-17.2017.159
ID  - Tian2016/06
ER  -