Details

Autor(en) / Beteiligte

• Xu, Liyang
• Wang, Dezheng

Titel

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

Ist Teil von

• PeerJ. Computer science, 2024-03, Vol.10, p.e1922, Article e1922

Ort / Verlag

PeerJ Inc

Erscheinungsjahr

2024

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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.