  The continued digitization of societal processes translates into a
proliferation of time series data that cover applications such as fraud
detection, intrusion detection, and energy management, where anomaly detection
is often essential to enable reliability and safety. Many recent studies target
anomaly detection for time series data. Indeed, area of time series anomaly
detection is characterized by diverse data, methods, and evaluation strategies,
and comparisons in existing studies consider only part of this diversity, which
makes it difficult to select the best method for a particular problem setting.
To address this shortcoming, we introduce taxonomies for data, methods, and
evaluation strategies, provide a comprehensive overview of unsupervised time
series anomaly detection using the taxonomies, and systematically evaluate and
compare state-of-the-art traditional as well as deep learning techniques. In
the empirical study using nine publicly available datasets, we apply the most
commonly-used performance evaluation metrics to typical methods under a fair
implementation standard. Based on the structuring offered by the taxonomies, we
report on empirical studies and provide guidelines, in the form of comparative
tables, for choosing the methods most suitable for particular application
settings. Finally, we propose research directions for this dynamic field.
