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.
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 |
Similar Items
-
Comparison of correlation analysis techniques for irregularly sampled time series
by: K. Rehfeld, et al.
Published: (2011-06-01) -
Estimation of sedimentary proxy records together with associated uncertainty
by: B. Goswami, et al.
Published: (2014-11-01) -
Every finite system of T1 uniformities comes from a single distance structure
by: Jobst Heitzig
Published: (2002-04-01) -
Finding recurrence networks' threshold adaptively for a specific time series
by: D. Eroglu, et al.
Published: (2014-11-01) -
A comparison of two methods for detecting abrupt changes in the variance of climatic time series
by: S. N. Rodionov
Published: (2016-06-01)