Time series networks: “Transforming messy big data into something comprehensible”

GSTDTAP > 资源环境科学

	Time series networks: “Transforming messy big data into something comprehensible”
	admin
	2019-02-04
发布年	2019
语种	英语
国家	德国
领域	资源环境
正文(英文)	04/02/2019 - A first in-depth review of time series networks has now been published by an international team of scientists in Physics Reports, one of the leading journals in its field. While both nonlinear time series analysis and complex networks theory are widely considered to be established areas of research, the combination of both approaches has now become an active field of scientific progress. The review discusses in great detail on more than 80 pages three main approaches. Examples that the authors touch upon reach from climatology to neurophysiology and economics. The team of authors is led by Yong Zou from East China Normal University, Shanghai, while all other authors are from the Potsdam Institute for Climate Impact research, including Jürgen Kurths, co-chair of the department for Complexity Science. Network representation of time series from the Lorenz attractor, a model describing atmospheric circulation dynamics (Source: Zou et al, 2019) “Time series networks are one way to extract patterns from data of very big volume or great variety. Artificial Intelligence is generating data in new forms of complexity, leading to the new era of big data”, says Kurths. “Advanced interdisciplinary data analysis techniques can help to capture the hidden structures in the midst of otherwise chaotic data points, including approaches from machine learning, data mining, statistics, language and text processing. In consequence, we transform the chaotic data sets into something comprehensible, which allows us making better and faster decisions.” The combination of network and time series analysis complements existing approaches and allows a more reliable understanding of causal interactions in the climate system, the detection of tipping points and early warning signals in a changing world, or the prediction of El Niño events. The Potsdam Institute aims at increasing the use of artificial intelligence to analyze big data in order to better understand how climate impacts hit people on the ground, and how the latter react to such shocks. This could help to better protect the public from such risks in the future. Article: Yong Zou, Reik V. Donner, Norbert Marwan, Jonathan F. Donges, Jürgen Kurths (2019): Complex network approaches to nonlinear time series analysis. Physics Reports 787 [DOI: /10.1016/j.physrep.2018.10.005] Weblink to the paper: https://doi.org/10.1016/j.physrep.2018.10.005
URL	查看原文
来源平台	Potsdam Institute for Climate Impact Research
文献类型	新闻
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/105175
专题	资源环境科学
推荐引用方式 GB/T 7714	admin. Time series networks: “Transforming messy big data into something comprehensible”. 2019.