ECA (Event Coincidence Analysis): Difference between revisions

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Created page with "<div style="text-align:justify"> '''Year of publication''': 2017 '''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 Analytic..."
 
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{{MHRA
'''Year of publication''': 2017
|Publication Year=2016
|Access=Open
|Link=https://link.springer.com/article/10.1140/epjst/e2015-50233-y
|Author(s)=Donges, J.F., Schleussner, C.F., Siegmund, J.F. and Donner, R.V.,
|Organisation(s)=Potsdam Institute for Climate Impact Research
|Description=Studying event time series offers a powerful method for examining the dynamics of complex systems across various scientific fields. Event coincidence analysis provides can be used to quantify the strength, directionality, and time lag of statistical relationships between event series. This approach enables the formulation and testing of null hypotheses regarding the origins of observed interrelationships, including tests based on Poisson processes or, more broadly, stochastic point processes with specified inter-event time distributions and other higher-order properties. Given the anticipated changes in the statistics of extreme climatic events, statistical techniques like event coincidence analysis will be essential for investigating the impacts of anthropogenic climate change on both human societies and ecosystems around the world.
|Technical Considerations=R package paper: https://www.sciencedirect.com/science/article/pii/S0098300416305489


'''Access''': Open
R package: https://github.com/JonatanSiegmund/CoinCalc
 
|Key Words=Event Coincidence Analysis, Time Series Analysis, Temporal Dependence
'''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
 
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Latest revision as of 16:32, 4 April 2025

Publication Year: 2016

Access: Open

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

Author(s): Donges, J.F., Schleussner, C.F., Siegmund, J.F. and Donner, R.V.,

Organisation(s)/Authors: Potsdam Institute for Climate Impact Research

Description:

Studying event time series offers a powerful method for examining the dynamics of complex systems across various scientific fields. Event coincidence analysis provides can be used to quantify the strength, directionality, and time lag of statistical relationships between event series. This approach enables the formulation and testing of null hypotheses regarding the origins of observed interrelationships, including tests based on Poisson processes or, more broadly, stochastic point processes with specified inter-event time distributions and other higher-order properties. Given the anticipated changes in the statistics of extreme climatic events, statistical techniques like event coincidence analysis will be essential for investigating the impacts of anthropogenic climate change on both human societies and ecosystems around the world.

Key Words:

Event Coincidence Analysis, Time Series Analysis, Temporal Dependence

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