  Detecting anomalies in time series data is important in a variety of fields,
including system monitoring, healthcare, and cybersecurity. While the abundance
of available methods makes it difficult to choose the most appropriate method
for a given application, each method has its strengths in detecting certain
types of anomalies. In this study, we compare six unsupervised anomaly
detection methods of varying complexity to determine whether more complex
methods generally perform better and if certain methods are better suited to
certain types of anomalies. We evaluated the methods using the UCR anomaly
archive, a recent benchmark dataset for anomaly detection. We analyzed the
results on a dataset and anomaly type level after adjusting the necessary
hyperparameters for each method. Additionally, we assessed the ability of each
method to incorporate prior knowledge about anomalies and examined the
differences between point-wise and sequence-wise features. Our experiments show
that classical machine learning methods generally outperform deep learning
methods across a range of anomaly types.
