  Explanation for Multivariate Time Series Classification (MTSC) is an
important topic that is under explored. There are very few quantitative
evaluation methodologies and even fewer examples of actionable explanation,
where the explanation methods are shown to objectively improve specific
computational tasks on time series data. In this paper we focus on analyzing
InterpretTime, a recent evaluation methodology for attribution methods applied
to MTSC. We reproduce the original paper results, showcase some significant
weaknesses of the methodology and propose ideas to improve both its accuracy
and efficiency. Unlike related work, we go beyond evaluation and also showcase
the actionability of the produced explainer ranking, by using the best
attribution methods for the task of channel selection in MTSC. We find that
perturbation-based methods such as SHAP and Feature Ablation work well across a
set of datasets, classifiers and tasks and outperform gradient-based methods.
We apply the best ranked explainers to channel selection for MTSC and show
significant data size reduction and improved classifier accuracy.
