  Anomaly detection for time-series data has been an important research field
for a long time. Seminal work on anomaly detection methods has been focussing
on statistical approaches. In recent years an increasing number of machine
learning algorithms have been developed to detect anomalies on time-series.
Subsequently, researchers tried to improve these techniques using (deep) neural
networks. In the light of the increasing number of anomaly detection methods,
the body of research lacks a broad comparative evaluation of statistical,
machine learning and deep learning methods. This paper studies 20 univariate
anomaly detection methods from the all three categories. The evaluation is
conducted on publicly available datasets, which serve as benchmarks for
time-series anomaly detection. By analyzing the accuracy of each method as well
as the computation time of the algorithms, we provide a thorough insight about
the performance of these anomaly detection approaches, alongside some general
notion of which method is suited for a certain type of data.
