Abrupt transitions in time series with uncertainties

Most time series techniques tend to ignore data uncertainties, which results in inaccurate conclusions. Here, Goswami et al. represent time series as a sequence of probability density functions, and reliably detect abrupt transitions by identifying communities in probabilistic recurrence networks.

Bibliographic Details
Main Authors:	Bedartha Goswami, Niklas Boers, Aljoscha Rheinwalt, Norbert Marwan, Jobst Heitzig, Sebastian F. M. Breitenbach, Jürgen Kurths
Format:	Article
Language:	English
Published:	Nature Publishing Group 2018-01-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-017-02456-6

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spelling	doaj-0e3d61b1efc947cfb08752dc628291d82021-05-11T09:34:52ZengNature Publishing GroupNature Communications2041-17232018-01-019111010.1038/s41467-017-02456-6Abrupt transitions in time series with uncertaintiesBedartha Goswami0Niklas Boers1Aljoscha Rheinwalt2Norbert Marwan3Jobst Heitzig4Sebastian F. M. Breitenbach5Jürgen Kurths6Potsdam Institute for Climate Impact Research, Transdisciplinary Concepts & MethodsPotsdam Institute for Climate Impact Research, Transdisciplinary Concepts & MethodsPotsdam Institute for Climate Impact Research, Transdisciplinary Concepts & MethodsPotsdam Institute for Climate Impact Research, Transdisciplinary Concepts & MethodsPotsdam Institute for Climate Impact Research, Transdisciplinary Concepts & MethodsSediment and Isotope Geology, Institute for Geology, Mineralogy & Geophysics, Ruhr-Universität BochumPotsdam Institute for Climate Impact Research, Transdisciplinary Concepts & MethodsMost time series techniques tend to ignore data uncertainties, which results in inaccurate conclusions. Here, Goswami et al. represent time series as a sequence of probability density functions, and reliably detect abrupt transitions by identifying communities in probabilistic recurrence networks.https://doi.org/10.1038/s41467-017-02456-6
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Bedartha Goswami Niklas Boers Aljoscha Rheinwalt Norbert Marwan Jobst Heitzig Sebastian F. M. Breitenbach Jürgen Kurths
spellingShingle	Bedartha Goswami Niklas Boers Aljoscha Rheinwalt Norbert Marwan Jobst Heitzig Sebastian F. M. Breitenbach Jürgen Kurths Abrupt transitions in time series with uncertainties Nature Communications
author_facet	Bedartha Goswami Niklas Boers Aljoscha Rheinwalt Norbert Marwan Jobst Heitzig Sebastian F. M. Breitenbach Jürgen Kurths
author_sort	Bedartha Goswami
title	Abrupt transitions in time series with uncertainties
title_short	Abrupt transitions in time series with uncertainties
title_full	Abrupt transitions in time series with uncertainties
title_fullStr	Abrupt transitions in time series with uncertainties
title_full_unstemmed	Abrupt transitions in time series with uncertainties
title_sort	abrupt transitions in time series with uncertainties
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2018-01-01
description	Most time series techniques tend to ignore data uncertainties, which results in inaccurate conclusions. Here, Goswami et al. represent time series as a sequence of probability density functions, and reliably detect abrupt transitions by identifying communities in probabilistic recurrence networks.
url	https://doi.org/10.1038/s41467-017-02456-6
work_keys_str_mv	AT bedarthagoswami abrupttransitionsintimeserieswithuncertainties AT niklasboers abrupttransitionsintimeserieswithuncertainties AT aljoscharheinwalt abrupttransitionsintimeserieswithuncertainties AT norbertmarwan abrupttransitionsintimeserieswithuncertainties AT jobstheitzig abrupttransitionsintimeserieswithuncertainties AT sebastianfmbreitenbach abrupttransitionsintimeserieswithuncertainties AT jurgenkurths abrupttransitionsintimeserieswithuncertainties
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Abrupt transitions in time series with uncertainties

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