  Multivariate time series classification is an important computational task
arising in applications where data is recorded over time and over multiple
channels. For example, a smartwatch can record the acceleration and orientation
of a person's motion, and these signals are recorded as multivariate time
series. We can classify this data to understand and predict human movement and
various properties such as fitness levels. In many applications classification
alone is not enough, we often need to classify but also understand what the
model learns (e.g., why was a prediction given, based on what information in
the data). The main focus of this paper is on analysing and evaluating
explanation methods tailored to Multivariate Time Series Classification (MTSC).
We focus on saliency-based explanation methods that can point out the most
relevant channels and time series points for the classification decision. We
analyse two popular and accurate multivariate time series classifiers, ROCKET
and dResNet, as well as two popular explanation methods, SHAP and dCAM. We
study these methods on 3 synthetic datasets and 2 real-world datasets and
provide a quantitative and qualitative analysis of the explanations provided.
We find that flattening the multivariate datasets by concatenating the channels
works as well as using multivariate classifiers directly and adaptations of
SHAP for MTSC work quite well. Additionally, we also find that the popular
synthetic datasets we used are not suitable for time series analysis.
