The reconstruction of equivalent underlying model based on direct causality for multivariate time series

This article presents a novel approach for reconstructing an equivalent underlying model and deriving a precise equivalent expression through the use of direct causality topology. Central to this methodology is the transfer entropy method, which is instrumental in revealing the causality topology. T...

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Veröffentlicht in:PeerJ. Computer science 2024-03, Vol.10, p.e1922, Article e1922
Hauptverfasser: Xu, Liyang, Wang, Dezheng
Format: Artikel
Sprache:eng
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Zusammenfassung:This article presents a novel approach for reconstructing an equivalent underlying model and deriving a precise equivalent expression through the use of direct causality topology. Central to this methodology is the transfer entropy method, which is instrumental in revealing the causality topology. The polynomial fitting method is then applied to determine the coefficients and intrinsic order of the causality structure, leveraging the foundational elements extracted from the direct causality topology. Notably, this approach efficiently discovers the core topology from the data, reducing redundancy without requiring prior domain-specific knowledge. Furthermore, it yields a precise equivalent model expression, offering a robust foundation for further analysis and exploration in various fields. Additionally, the proposed model for reconstructing an equivalent underlying framework demonstrates strong forecasting capabilities in multivariate time series scenarios.
ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.1922