Complex time series are increasingly prevalent in our daily lives. This project aims to advance the study of complex dynamics in such time series, specifically focusing on the application of state-transition networks (STNs) [Sándor et al. (2021)] in interdisciplinary problems spanning physics, biology, and financial markets.
The objectives are twofold: developing a theoretical framework for STNs to enhance our understanding of their properties, relationships with other encoding frameworks and measures of chaos theory, and their potential in detecting critical transitions; and expanding interdisciplinary applications by extending the methodology to nonstationary time series, nonautonomous dynamical systems, and exploring their relevance in areas such as functional brain networks, financial time series, and behavior-related experiments. By achieving these goals, this research contributes to the field of physics by providing insights into complex dynamics and utilizing STNs as a powerful computational tool in various time series datasets.
GO1. Advancing the theoretical foundations of state-transition networks.
GO2. Extending the range of applications of the framework of state-transition networks.
Principal Investigator: Bulcsú Sándor
Senior Researchers: Zsolt Lázár, Mária Ercsey-Ravasz
Postdocs: Botond Tyukodi, Botond Molnár
PhD Student: András Rusu
Master student: Tamás András
This work was supported by a grant of the Romanian Ministry of Education and Research, CNCS - UEFISCDI, project number PN-IV-P2-2.1-TE-2023-1548 within PNCDI IV.
B Sándor, B Schneider, ZI Lázár, M Ercsey-Ravasz, A novel measure inspired by lyapunov exponents for the characterization of dynamics in state-transition networks, Entropy 23 (1), 103