  This paper presents a new Python library for anomaly detection in
unsupervised learning approaches. The input for the library is a univariate
time series representing observations of a given phenomenon. Then, it can
identify anomalous epochs, i.e., time intervals where the observations are
above a given percentile of a baseline distribution, defined by a dissimilarity
metric. Using time-evolving graphs for the anomaly detection, the library
leverages valuable information given by the inter-dependencies among data.
Currently, the library implements 28 different dissimilarity metrics, and it
has been designed to be easily extended with new ones. Through an API, the
library exposes a complete functionality to carry out the anomaly detection.
Summarizing, to the best of our knowledge, this library is the only one
publicly available, that based on dynamic graphs, can be extended with other
state-of-the-art anomaly detection techniques. Our experimentation shows
promising results regarding the execution times of the algorithms and the
accuracy of the implemented techniques. Additionally, the paper provides
guidelines for setting the parameters of the detectors to improve their
performance and prediction accuracy.
