ECA (Event Coincidence Analysis)

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.

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