  Sensor-equipped beehives allow monitoring the living conditions of bees.
Machine learning models can use the data of such hives to learn behavioral
patterns and find anomalous events. One type of event that is of particular
interest to apiarists for economical reasons is bee swarming. Other events of
interest are behavioral anomalies from illness and technical anomalies, e.g.
sensor failure. Beekeepers can be supported by suitable machine learning models
which can detect these events. In this paper we compare multiple machine
learning models for anomaly detection and evaluate them for their applicability
in the context of beehives. Namely we employed Deep Recurrent Autoencoder,
Elliptic Envelope, Isolation Forest, Local Outlier Factor and One-Class SVM.
Through evaluation with real world datasets of different hives and with
different sensor setups we find that the autoencoder is the best multi-purpose
anomaly detector in comparison.
