  Change point detection is a crucial aspect of analyzing time series data, as
the presence of a change point indicates an abrupt and significant change in
the process generating the data. While many algorithms for the problem of
change point detection have been developed over time, it can be challenging to
select the appropriate algorithm for a specific problem. The choice of the
algorithm heavily depends on the nature of the problem and the underlying data
source. In this paper, we will exclusively examine unsupervised techniques due
to their flexibility in the application to various data sources without the
requirement for abundant annotated training data and the re-calibration of the
model. The examined methods will be introduced and evaluated based on several
criteria to compare the algorithms.
