  In current research, machine and deep learning solutions for the
classification of temporal data are shifting from single-channel datasets
(univariate) to problems with multiple channels of information (multivariate).
The majority of these works are focused on the method novelty and architecture,
and the format of the input data is often treated implicitly. Particularly,
multivariate datasets are often treated as a stack of univariate time series in
terms of input preprocessing, with scaling methods applied across each channel
separately. In this evaluation, we aim to demonstrate that the additional
channel dimension is far from trivial and different approaches to scaling can
lead to significantly different results in the accuracy of a solution. To that
end, we test seven different data transformation methods on four different
temporal dimensions and study their effect on the classification accuracy of
five recent methods. We show that, for the large majority of tested datasets,
the best transformation-dimension configuration leads to an increase in the
accuracy compared to the result of each model with the same hyperparameters and
no scaling, ranging from 0.16 to 76.79 percentage points. We also show that if
we keep the transformation method constant, there is a statistically
significant difference in accuracy results when applying it across different
dimensions, with accuracy differences ranging from 0.23 to 47.79 percentage
points. Finally, we explore the relation of the transformation methods and
dimensions to the classifiers, and we conclude that there is no prominent
general trend, and the optimal configuration is dataset- and
classifier-specific.
