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 integrate both dynamical and statistical methods to address this important challenge in a novel way.The proposed TCD approach,basing on exploitation of spatial information of the observed time series of high dimensions,is able not only to detect the separate change points of the concerned systems without knowing,a priori,any information of the equations of the systems,but also to harvest all the change points emergent in a relatively high-frequency manner,which cannot be directly achieved by using the existing methods and techniques.Practical effectiveness is comprehensively demonstrated using the data from the representative complex dynamics and real-world systems from biology to geology and even to social science.

Jia-Wen Hou;Huan-Fei Ma;Dake He;Jie Sun;Qing Nie;Wei Lin

Research Institute of Intelligent Complex Systems,Fudan University,Shanghai 200433,China;Centre for Computational Systems Biology,Institute of Science and Technology for Brain-Inspired Intelligence,Fudan University,Shanghai 200433,China;School of Mathematical Sciences,Soochow University,Suzhou 215006,China;Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine,Shanghai 200092,China;School of Mathematical Sciences and Shanghai Center for Mathematical Sciences,Fudan University,Shanghai 200433,China;Department of Mathematics,Department of Developmental and Cell Biology,and NSF-Simons Center for Multiscale Cell Fate Research,University of California,Irvine,CA 92697-3875,USA;Shanghai Key Laboratory for Contemporary Applied Mathematics,LNMS(Fudan University),and LCNBI(Fudan University),Shanghai 200433,China;State Key Laboratory of Medical Neurobiology,and MOE Frontiers Center for Brain Science,Institutes of Brain Science,Fudan University,Shanghai 200032,China

国家科学评论（英文版）

[1]Jia-Wen Hou;Huan-Fei Ma;Dake He;Jie Sun;Qing Nie;Wei Lin-.Harvesting random embedding for high-frequency change-point detection in temporal complex systems)[J].国家科学评论（英文版）,2022(04):87-99

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