  Time-series anomaly detection plays an important role in engineering
processes, like development, manufacturing and other operations involving
dynamic systems. These processes can greatly benefit from advances in the
field, as state-of-the-art approaches may aid in cases involving, for example,
highly dimensional data. To provide the reader with understanding of the
terminology, this survey introduces a novel taxonomy where a distinction
between online and offline, and training and inference is made. Additionally,
it presents the most popular data sets and evaluation metrics used in the
literature, as well as a detailed analysis. Furthermore, this survey provides
an extensive overview of the state-of-the-art model-based online semi- and
unsupervised anomaly detection approaches for multivariate time-series data,
categorising them into different model families and other properties. The
biggest research challenge revolves around benchmarking, as currently there is
no reliable way to compare different approaches against one another. This
problem is two-fold: on the one hand, public data sets suffers from at least
one fundamental flaw, while on the other hand, there is a lack of intuitive and
representative evaluation metrics in the field. Moreover, the way most
publications choose a detection threshold disregards real-world conditions,
which hinders the application in the real world. To allow for tangible advances
in the field, these issues must be addressed in future work.
