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

被引:19
作者
Hou, Jia-Wen [1 ,2 ]
Ma, Huan-Fei [3 ]
He, Dake [4 ]
Sun, Jie [1 ,5 ,6 ]
Nie, Qing [7 ,8 ]
Lin, Wei [1 ,2 ,5 ,6 ,9 ,10 ,11 ,12 ]
机构
[1] Fudan Univ, Res Inst Intelligent Complex Syst, Shanghai 200433, Peoples R China
[2] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Ctr Computat Syst Biol, Shanghai 200433, Peoples R China
[3] Soochow Univ, Sch Math Sci, Suzhou 115006, Peoples R China
[4] Shanghai Jiao Tong Univ, Sch Med, Xinhua Hosp, Shanghai 200092, Peoples R China
[5] Fudan Univ, Sch Math Sci, Shanghai 200433, Peoples R China
[6] Fudan Univ, Shanghai Ctr Math Sci, Shanghai 200433, Peoples R China
[7] Univ Calif Irvine, Dept Math, Dept Dev & Cell Biol, Irvine, CA 92697 USA
[8] Univ Calif Irvine, NSF Simons Ctr Multiscale Cell Fate Res, Irvine, CA 92697 USA
[9] Fudan Univ, LNMS, Shanghai Key Lab Contemporary Appl Math, Shanghai 200433, Peoples R China
[10] Fudan Univ, LCNBI, Shanghai 200433, Peoples R China
[11] Fudan Univ, State Key Lab Med Neurobiol, Inst Brain Sci, Shanghai 200032, Peoples R China
[12] Fudan Univ, Inst Brain Sci, MOE Frontiers Ctr Brain Sci, Shanghai 200032, Peoples R China
基金
国家重点研发计划; 美国国家科学基金会; 中国国家自然科学基金;
关键词
temporal systems; time series; change-point detection; complex dynamical systems; TIME-SERIES DATA; GREENLAND; CAUSATION; GRADIENTS; NETWORKS;
D O I
10.1093/nsr/nwab228
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:13
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