JSIAM Letters
Online ISSN : 1883-0617
Print ISSN : 1883-0609
ISSN-L : 1883-0617
Causal inference for empirical dynamical systems based on persistent homology
Hiroaki BandoShizuo KajiTakaharu Yaguchi
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2022 Volume 14 Pages 69-72

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Abstract

Given two correlated systems, detecting causality between them from observed data is an important but challenging task. Combining two mathematical techniques, delay coordinate embedding and persistent homology, we propose a novel causal inference method for data comprising a pair of scalar time series that are observed from two possibly coupled deterministic dynamical systems. The idea is to encode the topology of the dynamics in the form of the persistent homology of the reconstructed attractors and compare the involved systems by a metric defined on the persistent homology.

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© 2022, The Japan Society for Industrial and Applied Mathematics
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