@inproceedings{guanche2019attribution,
    type = {inproceedings},
    key = {guanche2019attribution},
    title = {Attribution of Multivariate Extreme Events},
    author = {Yanira Guanche and Maha Shadaydeh and Miguel Mahecha and Joachim Denzler},
    booktitle = {International Workshop on Climate Informatics (CI)},
    year = {2019},
    abstract = {The detection of multivariate extreme events is crucial to monitor the Earth system and to analyze their impacts on ecosystems and society. Once an abnormal event is detected, the following natural question is: what is causing this anomaly? Answering this question we try to understand these anomalies, to explain why they happened. In a previous work, the authors presented a multivariate anomaly detection approach based on the combination of a vector autoregressive model and the Mahalanobis distance metric. In this paper, we present an approach for the attribution of the detected anomalous events based on the decomposition of the Mahalanobis distance. The decomposed form of this metric provides an answer to the question: how much does each variable contribute to this distance metric? The method is applied to the extreme events detected in the land-atmosphere exchange fluxes: Gross Primary Productivity, Latent Energy, Net Ecosystem Exchange, Sensible Heat and Terrestrial Ecosystem Respiration. The attribution results of the proposed method for different known historic events are presented and compared with the univariate Z-score attribution method.},
    groups = {noveltydetection},
}