Abrupt transitions in time series with uncertainties

Bedartha Goswami, Niklas Boers, Aljoscha Rheinwalt, Norbert Marwan, Jobst Heitzig, Sebastian Breitenbach, Jürgen Kurths

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)
2 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number48
Pages (from-to)1-10
Number of pages10
JournalNature Communications
Volume9
DOIs
Publication statusPublished - 3 Jan 2018
Externally publishedYes

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