  The elasticity of the DTW metric provides a more flexible comparison between
time series and is used in numerous machine learning domains such as
classification or clustering. However, it does not align the measurements at
the beginning and end of time series if they have a shift occurring right at
the start of one series, with the omitted part appearing at the end of that
series. Due to the cyclicity of such series - which lack a definite beginning
or end - we rely on the Cyclic DTW approach to propose a less computationally
expensive approximation of this calculation method. This approximation will
then be employed in conjunction with the K-Means clustering method.
