Harvesting random embedding for high-frequency change-point detection in temporal complex systems.

ABSTRACT: Recent investigations have revealed that dynamics of complex networks and systems are crucially dependent on the temporal structures. Accurate detection of the time instant at which a system changes its internal structures has become a tremendously significant mission, beneficial to fully understanding the underlying mechanisms of evolving systems, and adequately modeling and predicting the dynamics of the systems as well. In real-world applications, due to a lack of prior knowledge on the explicit equations of evolving systems, an open challenge is how to develop a practical and model-free method to achieve the mission based merely on the time-series data recorded from real-world systems. Here, we develop such a model-free approach, named temporal change-point detection (TCD), and

SUBMITTER: Hou JW 

PROVIDER: S-EPMC9097594 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature