ArXiv Preprint
The detection of anomalies in time series data is crucial in a wide range of
applications, such as system monitoring, health care or cyber security. While
the vast number of available methods makes selecting the right method for a
certain application hard enough, different methods have different strengths,
e.g. regarding the type of anomalies they are able to find. In this work, we
compare six unsupervised anomaly detection methods with different complexities
to answer the questions: Are the more complex methods usually performing
better? And are there specific anomaly types that those method are tailored to?
The comparison is done on the UCR anomaly archive, a recent benchmark dataset
for anomaly detection. We compare the six methods by analyzing the experimental
results on a dataset- and anomaly type level after tuning the necessary
hyperparameter for each method. Additionally we examine the ability of
individual methods to incorporate prior knowledge about the anomalies and
analyse the differences of point-wise and sequence wise features. We show with
broad experiments, that the classical machine learning methods show a superior
performance compared to the deep learning methods across a wide range of
anomaly types.
Ferdinand Rewicki, Joachim Denzler, Julia Niebling
2022-12-21
