  Recently, continual learning has received a lot of attention. One of the
significant problems is the occurrence of \emph{concept drift}, which consists
of changing probabilistic characteristics of the incoming data. In the case of
the classification task, this phenomenon destabilizes the model's performance
and negatively affects the achieved prediction quality. Most current methods
apply statistical learning and similarity analysis over the raw data. However,
similarity analysis in streaming data remains a complex problem due to time
limitation, non-precise values, fast decision speed, scalability, etc. This
article introduces a novel method for monitoring changes in the probabilistic
distribution of multi-dimensional data streams. As a measure of the rapidity of
changes, we analyze the popular Kullback-Leibler divergence. During the
experimental study, we show how to use this metric to predict the concept drift
occurrence and understand its nature. The obtained results encourage further
work on the proposed methods and its application in the real tasks where the
prediction of the future appearance of concept drift plays a crucial role, such
as predictive maintenance.
