Abrupt transitions in time series with uncertainties
Identifying abrupt transitions is a key question in various disciplines. Existing transition detection methods, however, do not rigorously account for time series uncertainties, often neglecting them altogether or assuming them to be independent and qualitatively similar. Here, we introduce a novel...
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ftleibnizopen:oai:oai.leibnizopen.de:AN-Om4YBdbrxVwz69ma9 2023-05-15T17:33:26+02:00 Abrupt transitions in time series with uncertainties Goswami, B. Boers, N. Rheinwalt, A. Marwan, N. Heitzig, J. Breitenbach, S.F.M. Kurths, J. 2018 application/pdf https://doi.org/10.34657/3747 https://oa.tib.eu/renate/handle/123456789/5118 eng eng London : Nature Publishing Group CC BY 4.0 Unported https://creativecommons.org/licenses/by/4.0/ CC-BY Nature Communications 9 (2018), Nr. 1 ice detection method El Nino-Southern Oscillation Holocene ice rafting identification method network analysis Pacific Decadal Oscillation probability density function summer time series uncertainty analysis Article Asian climate community structure El Nino human time series analysis transition temperature uncertainty Atlantic Ocean Atlantic Ocean (North) 510 article Text 2018 ftleibnizopen https://doi.org/10.34657/3747 2023-03-01T07:47:14Z Identifying abrupt transitions is a key question in various disciplines. Existing transition detection methods, however, do not rigorously account for time series uncertainties, often neglecting them altogether or assuming them to be independent and qualitatively similar. Here, we introduce a novel approach suited to handle uncertainties by representing the time series as a time-ordered sequence of probability density functions. We show how to detect abrupt transitions in such a sequence using the community structure of networks representing probabilities of recurrence. Using our approach, we detect transitions in global stock indices related to well-known periods of politico-economic volatility. We further uncover transitions in the El Niño-Southern Oscillation which coincide with periods of phase locking with the Pacific Decadal Oscillation. Finally, we provide for the first time an 'uncertainty-aware' framework which validates the hypothesis that ice-rafting events in the North Atlantic during the Holocene were synchronous with a weakened Asian summer monsoon. publishedVersion Article in Journal/Newspaper North Atlantic LeibnizOpen (The Leibniz Association) Pacific |
institution |
Open Polar |
collection |
LeibnizOpen (The Leibniz Association) |
op_collection_id |
ftleibnizopen |
language |
English |
topic |
ice detection method El Nino-Southern Oscillation Holocene ice rafting identification method network analysis Pacific Decadal Oscillation probability density function summer time series uncertainty analysis Article Asian climate community structure El Nino human time series analysis transition temperature uncertainty Atlantic Ocean Atlantic Ocean (North) 510 |
spellingShingle |
ice detection method El Nino-Southern Oscillation Holocene ice rafting identification method network analysis Pacific Decadal Oscillation probability density function summer time series uncertainty analysis Article Asian climate community structure El Nino human time series analysis transition temperature uncertainty Atlantic Ocean Atlantic Ocean (North) 510 Goswami, B. Boers, N. Rheinwalt, A. Marwan, N. Heitzig, J. Breitenbach, S.F.M. Kurths, J. Abrupt transitions in time series with uncertainties |
topic_facet |
ice detection method El Nino-Southern Oscillation Holocene ice rafting identification method network analysis Pacific Decadal Oscillation probability density function summer time series uncertainty analysis Article Asian climate community structure El Nino human time series analysis transition temperature uncertainty Atlantic Ocean Atlantic Ocean (North) 510 |
description |
Identifying abrupt transitions is a key question in various disciplines. Existing transition detection methods, however, do not rigorously account for time series uncertainties, often neglecting them altogether or assuming them to be independent and qualitatively similar. Here, we introduce a novel approach suited to handle uncertainties by representing the time series as a time-ordered sequence of probability density functions. We show how to detect abrupt transitions in such a sequence using the community structure of networks representing probabilities of recurrence. Using our approach, we detect transitions in global stock indices related to well-known periods of politico-economic volatility. We further uncover transitions in the El Niño-Southern Oscillation which coincide with periods of phase locking with the Pacific Decadal Oscillation. Finally, we provide for the first time an 'uncertainty-aware' framework which validates the hypothesis that ice-rafting events in the North Atlantic during the Holocene were synchronous with a weakened Asian summer monsoon. publishedVersion |
format |
Article in Journal/Newspaper |
author |
Goswami, B. Boers, N. Rheinwalt, A. Marwan, N. Heitzig, J. Breitenbach, S.F.M. Kurths, J. |
author_facet |
Goswami, B. Boers, N. Rheinwalt, A. Marwan, N. Heitzig, J. Breitenbach, S.F.M. Kurths, J. |
author_sort |
Goswami, B. |
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 |
London : Nature Publishing Group |
publishDate |
2018 |
url |
https://doi.org/10.34657/3747 https://oa.tib.eu/renate/handle/123456789/5118 |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Nature Communications 9 (2018), Nr. 1 |
op_rights |
CC BY 4.0 Unported https://creativecommons.org/licenses/by/4.0/ |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.34657/3747 |
_version_ |
1766131942863929344 |