Simple correlation dimension estimator and its use to detect causality

Krakovská, Anna; Chvosteková, Martina

doi:10.1016/j.chaos.2023.113975

Chaos, Solitons & Fractals

2023

DOI: 10.1016/j.chaos.2023.113975

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Simple correlation dimension estimator and its use to detect causality

Anna Krakovská,

Martina Chvosteková

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2024

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Cited by 2 publications

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Decoding compositional complexity: Identifying composers using a model fusion-based approach with nonlinear signal processing and chaotic dynamics

Mirza,

Baykaş,

Hekimoğlu

et al. 2024

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

Decoding compositional complexity: Identifying composers using a model fusion-based approach with nonlinear signal processing and chaotic dynamics

Mirza,

Baykaş,

Hekimoğlu

et al. 2024

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

Fractal Conditional Correlation Dimension Infers Complex Causal Networks

Canlı Usta,

Bollt

2024

Entropy

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Determining causal inference has become popular in physical and engineering applications. While the problem has immense challenges, it provides a way to model the complex networks by observing the time series. In this paper, we present the optimal conditional correlation dimensional geometric information flow principle (oGeoC) that can reveal direct and indirect causal relations in a network through geometric interpretations. We introduce two algorithms that utilize the oGeoC principle to discover the direct links and then remove indirect links. The algorithms are evaluated using coupled logistic networks. The results indicate that when the number of observations is sufficient, the proposed algorithms are highly accurate in identifying direct causal links and have a low false positive rate.

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Simple correlation dimension estimator and its use to detect causality

Cited by 2 publications

References 28 publications

Decoding compositional complexity: Identifying composers using a model fusion-based approach with nonlinear signal processing and chaotic dynamics

Decoding compositional complexity: Identifying composers using a model fusion-based approach with nonlinear signal processing and chaotic dynamics

Fractal Conditional Correlation Dimension Infers Complex Causal Networks

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