Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
  Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben
Konferenzbeitrag

Maximally divergent intervals for anomaly detection

MPG-Autoren
/persons/resource/persons195348

Flach,  Milan
Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons62472

Mahecha,  Miguel D.
Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;
Michael Stifel Center for Data-driven and Simulation Science, Jena, Germany;
Empirical Inference of the Earth System, Dr. Miguel D. Mahecha, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons197682

Bodesheim,  Paul
Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons62524

Reichstein,  Markus
Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;
Michael Stifel Center for Data-driven and Simulation Science, Jena, Germany;

Externe Ressourcen

https://arxiv.org/abs/1610.06761
(Verlagsversion)

Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)

BGC2496.pdf
(Verlagsversion), 384KB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Rodner, E., Barz, B., Guanche, Y., Flach, M., Mahecha, M. D., Bodesheim, P., et al. (2016). Maximally divergent intervals for anomaly detection. In ICML 2016 Anomaly Detection Workshop. doi:10.17871/BACI_ICML2016_Rodner.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-002B-4F5E-B
Zusammenfassung
We present new methods for batch anomaly detection in multivariate time series. Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution within and outside an interval of the time series. An empirical analysis shows the benefits of our algorithms compared to methods that treat each time step independently from each other without optimizing with respect to all possible intervals.