TY - JOUR
T1 - Higher-order organization of multivariate time series
AU - Santoro, Andrea
AU - Battiston, Federico
AU - Petri, Giovanni
AU - Amico, Enrico
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2023/1/2
Y1 - 2023/1/2
N2 - Time series analysis has proven to be a powerful method to characterize several phenomena in biology, neuroscience and economics, and to understand some of their underlying dynamical features. Several methods have been proposed for the analysis of multivariate time series, yet most of them neglect the effect of non-pairwise interactions on the emerging dynamics. Here, we propose a framework to characterize the temporal evolution of higher-order dependencies within multivariate time series. Using network analysis and topology, we show that our framework robustly differentiates various spatiotemporal regimes of coupled chaotic maps. This includes chaotic dynamical phases and various types of synchronization. Hence, using the higher-order co-fluctuation patterns in simulated dynamical processes as a guide, we highlight and quantify signatures of higher-order patterns in data from brain functional activity, financial markets and epidemics. Overall, our approach sheds light on the higher-order organization of multivariate time series, allowing a better characterization of dynamical group dependencies inherent to real-world data.
AB - Time series analysis has proven to be a powerful method to characterize several phenomena in biology, neuroscience and economics, and to understand some of their underlying dynamical features. Several methods have been proposed for the analysis of multivariate time series, yet most of them neglect the effect of non-pairwise interactions on the emerging dynamics. Here, we propose a framework to characterize the temporal evolution of higher-order dependencies within multivariate time series. Using network analysis and topology, we show that our framework robustly differentiates various spatiotemporal regimes of coupled chaotic maps. This includes chaotic dynamical phases and various types of synchronization. Hence, using the higher-order co-fluctuation patterns in simulated dynamical processes as a guide, we highlight and quantify signatures of higher-order patterns in data from brain functional activity, financial markets and epidemics. Overall, our approach sheds light on the higher-order organization of multivariate time series, allowing a better characterization of dynamical group dependencies inherent to real-world data.
UR - http://www.scopus.com/inward/record.url?scp=85145414125&partnerID=8YFLogxK
U2 - 10.1038/s41567-022-01852-0
DO - 10.1038/s41567-022-01852-0
M3 - Article
AN - SCOPUS:85145414125
SN - 1745-2473
VL - 19
SP - 221
EP - 229
JO - Nature Physics
JF - Nature Physics
IS - 2
ER -