Year: 2021
Author: Huanfei Ma, Siyang Leng, Luonan Chen
CSIAM Transactions on Applied Mathematics, Vol. 2 (2021), Iss. 4 : pp. 680–696
Abstract
Reconstructing faithfully causal networks from observed time series data is fundamental to revealing the intrinsic nature of complex systems. With the increase of the network scale, indirect causal relations will arise due to causation transitivity but existing methods suffer from dimension curse in eliminating such indirect influences. In this paper, we propose a novel technique to overcome the difficulties by integrating the idea of randomly distributed embedding into conditional Granger causality. Validated by both benchmark and synthetic data sets, our method demonstrates potential applicability in reconstructing high-dimensional causal networks based only on a short-term time series.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/csiam-am.2020-0184
CSIAM Transactions on Applied Mathematics, Vol. 2 (2021), Iss. 4 : pp. 680–696
Published online: 2021-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 17
Keywords: Network reconstruction Granger causality conditional causality randomly distributed embedding.
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