ECA (Event Coincidence Analysis) - Revision history

Myriad-admin at 15:32, 4 April 2025

2025-04-04T15:32:22Z

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	\|Access=Open		\|Access=Open
	\|Link=https://link.springer.com/article/10.1140/epjst/e2015-50233-y		\|Link=https://link.springer.com/article/10.1140/epjst/e2015-50233-y
	\|~~Organisation~~(s)~~/Authors~~=~~otsdam 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~~		\|Author(s)=Donges, J.F., Schleussner, C.F., Siegmund, J.F. and Donner, R.V.,
	\|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~~.		\|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		\|Technical Considerations=R package paper: https://www.sciencedirect.com/science/article/pii/S0098300416305489

Myriad-admin at 15:53, 25 March 2025

2025-03-25T15:53:27Z

← Older revision		Revision as of 16:53, 25 March 2025
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	\|Key Words=Event Coincidence Analysis, Time Series Analysis, Temporal Dependence		\|Key Words=Event Coincidence Analysis, Time Series Analysis, Temporal Dependence
	}}		}}
	~~<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>~~

Myriad-admin at 15:53, 25 March 2025

2025-03-25T15:53:11Z

← Older revision		Revision as of 16:53, 25 March 2025
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			{{MHRA
			\|Publication Year=2016
			\|Access=Open
			\|Link=https://link.springer.com/article/10.1140/epjst/e2015-50233-y
			\|Organisation(s)/Authors=otsdam 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
			\|Key Words=Event Coincidence Analysis, Time Series Analysis, Temporal Dependence
			}}
	<div style="text-align:justify">		<div style="text-align:justify">
	'''Year of publication''': 2016		'''Year of publication''': 2016

Myriad-admin at 13:42, 3 July 2024

2024-07-03T13:42:17Z

← Older revision		Revision as of 14:42, 3 July 2024
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	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		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		'''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.
	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		'''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		'''Keywords''': Event Coincidence Analysis, Time Series Analysis, Temporal Dependence

	[[Category:~~CategoryPageName~~]]		[[Category:Multi-hazard Risk Assessment]]
	</div>		</div>

Hquintal at 15:19, 1 July 2024

2024-07-01T15:19:07Z

← Older revision		Revision as of 16:19, 1 July 2024
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	<div style="text-align:justify">		<div style="text-align:justify">
	'''Year of publication''': ~~2017~~		'''Year of publication''': 2016

	'''Access''': Open		'''Access''': Open

Hquintal: Created page with "
'''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..."

2024-07-01T15:18:32Z

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..."

New page

<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 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:CategoryPageName]]
</div>