  Data series classification is an important and challenging problem in data
science. Explaining the classification decisions by finding the discriminant
parts of the input that led the algorithm to some decisions is a real need in
many applications. Convolutional neural networks perform well for the data
series classification task; though, the explanations provided by this type of
algorithm are poor for the specific case of multivariate data series.
Addressing this important limitation is a significant challenge. In this paper,
we propose a novel method that solves this problem by highlighting both the
temporal and dimensional discriminant information. Our contribution is
two-fold: we first describe a convolutional architecture that enables the
comparison of dimensions; then, we propose a method that returns dCAM, a
Dimension-wise Class Activation Map specifically designed for multivariate time
series (and CNN-based models). Experiments with several synthetic and real
datasets demonstrate that dCAM is not only more accurate than previous
approaches, but the only viable solution for discriminant feature discovery and
classification explanation in multivariate time series. This paper has appeared
in SIGMOD'22.
