@inproceedings{Barz17:MDI-Weather,
    type = {inproceedings},
    key = {Barz17:MDI-Weather},
    title = {Maximally Divergent Intervals for Extreme Weather Event Detection},
    author = {Björn Barz and Yanira Guanche and Erik Rodner and Joachim Denzler},
    booktitle = {MTS/IEEE OCEANS Conference Aberdeen},
    year = {2017},
    month = {June},
    pages = {1-9},
    abstract = {We approach the task of detecting anomalous or extreme events in multivariate spatio-temporal climate data using an unsupervised machine learning algorithm for detection of anomalous intervals in time-series. In contrast to many existing algorithms for outlier and anomaly detection, our method does not search for point-wise anomalies, but for contiguous anomalous intervals. We demonstrate the suitability of our approach through numerous experiments on climate data, including detection of hurricanes, North Sea storms, and low-pressure fields.},
    doi = {10.1109/OCEANSE.2017.8084569},
    groups = {noveltydetection},
}