Detecting shifts in nonlinear dynamics using Empirical Dynamic Modeling with Nested-Library Analysis.

ABSTRACT: Abrupt changes in system states and dynamical behaviors are often observed in natural systems; such phenomena, named regime shifts, are explained as transitions between alternative steady states (more generally, attractors). Various methods have been proposed to detect regime shifts from time series data, but a generic detection method with theoretical linkage to underlying dynamics is lacking. Here, we provide a novel method named Nested-Library Analysis (NLA) to retrospectively detect regime shifts using empirical dynamic modeling (EDM) rooted in theory of attractor reconstruction. Specifically, NLA determines the time of regime shift as the cutting point at which sequential reduction of the library set (i.e., the time series data used to reconstruct the attractor for forecasting) optimi

SUBMITTER: Huang YJ 

PROVIDER: S-EPMC10795988 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature