Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data¶
Dakos, V., Carpenter, S. R., Brock, W. A., Ellison, A. M., Guttal, V., Ives, A. R., Kéfi, S., et al. (2012). Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE, 7(7).
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Criticality
- The second is early-warning detection: critical slowing-down, rising variance, and rising autocorrelation are model-independent precursors of an approach to a second-order critical point, exploitable in ecology, finance, epidemiology, and physiology, an inferential framework Dakos et al. (2012) formalize as a methodological toolkit for time-series detection of approaching transitions.
Supported in partVerified against the work's full text
Supports a generic time-series toolkit of variance and autocorrelation indicators for approaching critical transitions; the paper concerns fold bifurcations and a simulated ecological model.
“Such indicators are based on common mathematical properties of phenomena that appear in a broad range of systems as they approach a catastrophic bifurcation”
- The second is early-warning detection: critical slowing-down, rising variance, and rising autocorrelation are model-independent precursors of an approach to a second-order critical point, exploitable in ecology, finance, epidemiology, and physiology, an inferential framework Dakos et al. (2012) formalize as a methodological toolkit for time-series detection of approaching transitions.
- Tipping Points (or Phase Transitions)
- Robust monitoring requires domain-specific signal validation and acceptance of fundamental limits to pre-crossing predictability.
This sourceEmpirical evaluation of early-warning signal detection methods; documents inconsistency of signals across real-world systems and noise sensitivity.
Supported in partVerified against the work's full text
Dakos et al. support the general point that early-warning signals need data-specific treatment and cannot remove false or missed alarms, but they tested only simulated data.
“Our results caution, however, that in all cases the performance of any method, as well as the interpretations based on them, will strongly depend on the characteristics of the actual time series tested.”
- Robust monitoring requires domain-specific signal validation and acceptance of fundamental limits to pre-crossing predictability.
Verification¶
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