ECA (Event Coincidence Analysis): Difference between revisions

Revision as of 16:53, 25 March 2025

Publication Year: 2016

Access: Open

Link: https://link.springer.com/article/10.1140/epjst/e2015-50233-y

Author(s):

Organisation(s)/Authors:

Description:

Studying event time series is a powerful approach for analyzing the dynamics of complex dynamical systems in many fields of science. The method of event coincidence analysis provides a framework for quantifying the strength, directionality and time lag of statistical interrelationships between event series. Event coincidence analysis allows to formulate and test null hypotheses on the origin of the observed interrelationships including tests based on Poisson processes or, more generally, stochastic point processes with a prescribed inter-event time distribution and other higher-order properties. Facing projected future changes in the statistics of climatic extreme events, statistical techniques such as event coincidence analysis will be relevant for investigating the impacts of anthropogenic climate change on human societies and ecosystems worldwide.

Technical Considerations:

R package paper: https://www.sciencedirect.com/science/article/pii/S0098300416305489 R package: https://github.com/JonatanSiegmund/CoinCalc

Key Words:

Event Coincidence Analysis, Time Series Analysis, Temporal Dependence

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 |Key Words=Event Coincidence Analysis, Time Series Analysis, Temporal Dependence
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-<div style="text-align:justify">
-'''Year of publication''': 2016
-'''Access''': Open
-'''Link''': https://link.springer.com/article/10.1140/epjst/e2015-50233-y
-'''Organisation(s) / Author(s)''': Potsdam Institute for Climate Impact Research, Telegrafenberg A31, 14473 Potsdam,
-Germany / J.F. Donges,  J.F. Siegmund, C.-F. Schleussner, R.V. Donner; Stockholm Resilience Centre, Stockholm University, Kr¨aftriket 2B, 114 19 Stockholm, Sweden / J.F. Donges, ; Climate Analytics, Friedrichstr. 231, Haus B, 10969 Berlin, Germany / , C.-F. Schleussner; Institute of Earth and Environmental Science, University of Potsdam,Karl-Liebknecht-Str. 24-25, 14476 Potsdam-Golm, Germany /  J.F. Siegmund
-'''Description''': Studying event time series is a powerful approach for analyzing the dynamics of complex dynamical systems in many fields of science. The method of event coincidence analysis provides a framework for quantifying the strength, directionality and time lag of statistical interrelationships between event series. Event coincidence analysis allows to formulate and test null hypotheses on the origin of the observed interrelationships including tests based on Poisson processes or, more generally, stochastic point processes with a prescribed inter-event time distribution and other higher-order properties. Facing projected future changes in the statistics of climatic extreme events, statistical techniques such as event coincidence analysis will be relevant for investigating the impacts of anthropogenic climate change on human societies and ecosystems worldwide.
-'''Technical considerations''': R package paper: https://www.sciencedirect.com/science/article/pii/S0098300416305489
-R package: https://github.com/JonatanSiegmund/CoinCalc
-'''Keywords''': Event Coincidence Analysis, Time Series Analysis, Temporal Dependence
-[[Category:Multi-hazard Risk Assessment]]
-</div>