  Anomaly detection in time series is a complex task that has been widely
studied. In recent years, the ability of unsupervised anomaly detection
algorithms has received much attention. This trend has led researchers to
compare only learning-based methods in their articles, abandoning some more
conventional approaches. As a result, the community in this field has been
encouraged to propose increasingly complex learning-based models mainly based
on deep neural networks. To our knowledge, there are no comparative studies
between conventional, machine learning-based and, deep neural network methods
for the detection of anomalies in multivariate time series. In this work, we
study the anomaly detection performance of sixteen conventional, machine
learning-based and, deep neural network approaches on five real-world open
datasets. By analyzing and comparing the performance of each of the sixteen
methods, we show that no family of methods outperforms the others. Therefore,
we encourage the community to reincorporate the three categories of methods in
the anomaly detection in multivariate time series benchmarks.
