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Maximally divergent intervals for extreme weather event detection

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Citation

Barz, B., Garcia, Y. G., Rodner, E., & Denzler, J. (2017). Maximally divergent intervals for extreme weather event detection. In OCEANS 2017 - Aberdeen. Aberdeen, UK: IEEE Oceanic Engineering Society.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002E-3772-C
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.