EGU2020-9554
https://doi.org/10.5194/egusphere-egu2020-9554
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Recent progress and new methods for detecting causal relations in large nonlinear time series datasets

Jakob Runge
Jakob Runge
  • Deutsches Zentrum für Luft- und Raumfahrt e.V., Jena, Germany (jakob.runge@dlr.de)

Detecting causal relationships from observational time series datasets is a key problem in better understanding the complex dynamical system Earth. Recent methodological advances have addressed major challenges such as high-dimensionality and nonlinearity, e.g., PCMCI (Runge et al. Sci. Adv. 2019), but many more remain. In this talk I will give an overview of challenges and methods and present a novel algorithm to identify causal directions among contemporaneous (or instantaneous) relationships. Such contemporaneous relations frequently appear when time series are aggregated (e.g., at a monthly resolution). Then approaches such as Granger Causality and PCMCI fail because they currently only address time-lagged causal relations.
We present extensive numerical examples and results on the causal relations among major climate modes of variability. The work overcomes a major drawback of current causal discovery methods and opens up entirely new possibilities to discover causal relations from time series in climate research and other fields in geosciences.

Runge et al., Detecting and quantifying causal associations in large nonlinear time series datasets, Science Advances eeaau4996 (2019).

How to cite: Runge, J.: Recent progress and new methods for detecting causal relations in large nonlinear time series datasets, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9554, https://doi.org/10.5194/egusphere-egu2020-9554, 2020

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Presentation version 1 – uploaded on 03 May 2020
  • CC1: Comment on EGU2020-9554, Scarlet Stadtler, 04 May 2020

    Hello, 

    I was just looking at some displays and I really liked how you desinged your slides. Thanks also for the extensive explanations and references to further reading. 

    I will keep your approach in mind, maybe there will be a way for me to use it in the future. 

     

    Keep safe!

    Scarlet