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Time-Frequency Causal Inference Uncovers Anomalous Events in Environmental Systems

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Pattern Recognition(DAGM GCPR 2019)

Abstract

Causal inference in dynamical systems is a challenge for different research areas. So far it is mostly about understanding to what extent the underlying causal mechanisms can be derived from observed time series. Here we investigate whether anomalous events can also be identified based on the observed changes in causal relationships. We use a parametric time-frequency representation of vector autoregressive Granger causality for causal inference. The use of time-frequency approach allows for dealing with the nonstationarity of the time series as well as for defining the time scale on which changes occur. We present two representative examples in environmental systems: land-atmosphere ecosystem and marine climate. We show that an anomalous event can be identified as the event where the causal intensities differ according to a distance measure from the average causal intensities. The driver of the anomalous event can then be identified based on the analysis of changes in the causal effect relationships.

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Acknowledgments

The authors thank the Carl Zeiss Foundation for the financial support within the scope of the program line “Breakthroughs: Exploring Intelligent Systems” for “Digitization—explore the basics, use applications”. This work used eddy covariance data acquired and shared by the FLUXNET community.

Author information

Authors and Affiliations

  1. Computer Vision Group, Friedrich Schiller University, Jena, Germany

    Maha Shadaydeh & Joachim Denzler

  2. Institute of Data Science, German Aerospace Center, DLR, Jena, Germany

    Yanira Guanche García

  3. Max Planck Institute for Biogeochemistry, Jena, Germany

    Miguel Mahecha

  4. Michael Stifel Center for Data driven and Simulation Science, Jena, Germany

    Joachim Denzler, Yanira Guanche García & Miguel Mahecha

Authors
  1. Maha Shadaydeh
  2. Joachim Denzler
  3. Yanira Guanche García
  4. Miguel Mahecha

Corresponding author

Correspondence toMaha Shadaydeh.

Editor information

Editors and Affiliations

  1. TU Dortmund University, Dortmund, Germany

    Gernot A. Fink

  2. University of Hamburg, Hamburg, Germany

    Simone Frintrop

  3. University of Münster, Münster, Germany

    Xiaoyi Jiang

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Shadaydeh, M., Denzler, J., García, Y.G., Mahecha, M. (2019). Time-Frequency Causal Inference Uncovers Anomalous Events in Environmental Systems. In: Fink, G., Frintrop, S., Jiang, X. (eds) Pattern Recognition. DAGM GCPR 2019. Lecture Notes in Computer Science(), vol 11824. Springer, Cham. https://doi.org/10.1007/978-3-030-33676-9_35

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